Meta-cognitive Neural Network based Sequential

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Procedia Computer Science 132 (2018) 1503–1511

International Conference on Computational Intelligence and Data Science (ICCIDS 2018) International Conference on Computational Intelligence and Data Science (ICCIDS 2018)

Meta-cognitive Neural Network based Sequential Learning Meta-cognitive Neural Network based Sequential Learning Framework for Text Categorization Framework for Text Categorization M B Revanasiddappa*, B S Harish, S V Aruna Kumar M B Revanasiddappa*, B S Harish, S V Aruna Kumar

Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, India Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, India

Abstract Abstract A sequential learning framework for text categorization based on Meta-cognitive Neural Network (McNN) is presented in this A sequential framework text categorization based oninMeta-cognitive Neural Network (McNN) is presented this paper. Initiallylearning text documents arefor pre-processed and represented the form of Term Document Matrix (TDM). Since theinTDM paper. Initially text documents areitpre-processed and represented in theLocality form ofPreserving Term Document Matrix (TDM). SinceFurther, the TDM is of high dimension, to reduce to lower dimension Regularized Indexing (RLPI) is used. to categorize text document, Meta-cognitive Neural Network (McNN) classifier is employed. To measure theused. effectiveness is of high the dimension, to reduce it to lower dimension Regularized Locality Preserving Indexing (RLPI) is Further, of to categorize theframework, text document, Meta-cognitive Networkon(McNN) is employed. To measure of the proposed various experiments Neural are conducted standardclassifier benchmark Reuters-21578 dataset the andeffectiveness used leave one the framework, various experiments are conducted standard benchmark Reuters-21578 dataset andagainst used leave one out proposed cross validation technique to assess the performance. The on proposed framework performance is investigated two well out cross validation technique to assess the performance. ThePerceptron) proposed framework performance is investigated againstNetwork). two well known neural network based classifiers: MLP (Multi Layer and RBF-NN (Radial Basis Function-Neural known neural network classifiers: MLP (Multi Layer Perceptron) andofRBF-NN (Radial Basis Function-Neural The experimental resultsbased reveals that the McNN classifier uses less number training documents for learning and it hasNetwork). less true The experimental results revealsnetwork that theclassifiers. McNN classifier uses less number of training documents for learning and it has less true error rate than other two neural error rate than other two neural network classifiers. © © 2018 2018 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/) © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018). Keywords: Sequential Learning, Meta-cognitive Neural Network, Text Categorization; Keywords: Sequential Learning, Meta-cognitive Neural Network, Text Categorization;

* Corresponding author. Tel.: +91-821-2548285; fax: 091-821-2548290. E-mail address:author. [email protected];[email protected];[email protected] * Corresponding Tel.: +91-821-2548285; fax: 091-821-2548290. E-mail address: [email protected];[email protected];[email protected]

1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018). 10.1016/j.procs.2018.05.104

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1. Introduction Text Categorization (TC) is a task of assigning unknown text document into pre-defined known classes based on its content [36, 8, 28, 5]. From past two decades, text categorization has been taken more attention by researchers, due to its capability of managing and organizing ever increasing text documents over the internet [21][19]. Major applications of Text Categorization: it helps to discover useful information in search engine websites, Automatic Document Indexing, Information Retrieval, Spam filtering etc. In literature, wide variety approaches have been developed for effective Text Categorization [11] viz., Naive Bayes (NB) [6], k-Nearest Neighbor (kNN) [16], Decision Tree (DT) [2], Centroid based Classifier (CbC) [23], Symbolic Classifier (SC) [7][10] [34], Support Vector Machine (SVM) [22][17]. Several studies using Neural Network have shown promising results including Multi-layer Perceptron (MLP) [26], Back-Propagation Neural Network (BP-NN) [31], Radial Basis Function Neural Network (RBF-NN) [14]. The categorization approaches can be broadly partitioned into batch and sequential based on the type of learning approach adopted [35]. In batch learning process complete training data is trained using several iterations. The most of the existing Neural Network based text classifiers are based on batch learning process. Even though existing batch learning Neural Network classifiers perform better, they suffer from following limitations: (i) Before start of learning process they requires complete training documents. However, in real time to obtain complete training documents in prior is very difficult. (ii) The structure of the network has to be defined in prior to learning process. The prior fixed network structure may cause the distortion in performance of the classifier and lastly, (iii) it requires high computation time for large training documents. In addition when a new train document arrives, it requires re-training [20][39][37]. To address the aforementioned limitations, research community developed sequential learning based ANN approaches for text categorization [3]. In sequential leaning, the training documents presented one-by-one and only one time to the network. The documents are eliminated after the leaning process completed by network. In addition, sequential learning techniques automatically find out the minimal network framework [40]. This framework can exactly approximate the true decision function presented by a stream of training documents. Radial Basis Function Neural Network (RBFNN) is one of the popular sequential learning architecture, due to its simplicity of framework and global approximation ability [12][27]. In literature, many researchers have used Radial Basis Function in different categorization problems [13][15][24]. Jiang [13] proposed new spam filtering model based using RBF named as RBF-SF, which categorizes the spam e-mails based on their content. Jiang [15] proposed a semi-supervised RBF classifier. This classifier is integrating clustering based Expectation Maximization (EM) technique with RBF-NN. This classifier reduces the dimensionality of feature space by make use of feature selection method. Further, it computes RBF middle layer parameters by applying clustering technique to both labeled and unlabeled text data. Finally, RBF-NN output weights are determined using regression model. Motwani et al., [24] presents the text classification method, which investigate the performance of Back-Propagation Neural Network (BP-NN) and Radial Basis Function Neural Network (RBF-NN). This method used term weighting schemas (term-frequency and term frequency-relevancy frequency) on BP-NN and RBF-NN. The experimental result revealed that RBF-NN shows better results with term frequency as compared to relevancy frequency. The contemporary research theories in human learning demonstrated that when the learner adopts self regulations using meta-cognition, then the learning process is more effective [38]. The term meta-cognition is defined as the “ones knowledge concerning ones own cognitive processes or anything related to them”[3]. In a Meta-cognition, human being thinks about their cognitive processes, formulate the new strategies to increase their cognitive skills and assess the information presented in their memory. Meta-cognition present in human-being provides a means to address what-to-learn, when-to-learn and how-to-learn, i.e., the ability to find out particular part of required knowledge, further to decide when to start and stop learning and emphasizing better learning strategy [3]. McNN is based on the human learning principles. McNN has two units: cognitive and meta-cognitive. Cognitive unit contains a radial basis function neural network. Whereas, meta-cognitive unit has a replica of cognitive unit and learning measures. Based on the learning measure, meta-cognitive unit adopts suitable learning strategies for cognitive unit. In literature, many works are reported on Meta-cognitive Neural Network (McNN) in different domains. McNN shows promising results compared to other ANN based approaches and other classifiers. In addition, McNN also capable of addressing human like learning strategies [29][30]. Motivated by the advantages of McNN in this paper, a new sequential learning framework for text categorization is developed. The main advantages of proposed



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framework are (i) It is sequential learning methods which not requires all the training documents prior to learning process. (ii) When a new training document arrive no need for re-training (iii) It is based on human learning principles (iv) Unlike other neural networks which uses all training documents for learning the proposed framework adopts suitable learning strategy and selects the document for learning. In this paper, the categorization process starts with elimination of stop-words and stemming during preprocessing. After pre-processing Term Document Matrix (TDM) is used to represent text documents. Since the TDM is of higher dimension, to reduce it to lower dimension feature selection method is employed i.e., Regularized Locality Preserving Indexing (RLPI) [4][9]. Further, Meta-cognitive Neural Network (McNN) is employed to categorize text documents. McNN consist two units. The first unit is cognitive unit, which is a single hidden layer and uses Gaussian activation function. The second unit is meta-cognitive, which measures the knowledge present in the training documents. The meta-cognitive part adopts suitable learning strategy based on the learning measures. The proposed frame work is experimented on Reuters-21578 standard benchmark dataset to assess the performance and used classification error as performance evaluation metric. The performance of proposed framework is investigated against two well known classifiers (MLP and RBF-NN). To the best of our knowledge this work is first of its kind, where McNN classifier is employed to categorize the text document. The rest of the paper is as follows. Section 2, describes the methodology of McNN classifier to categorize text document. The experimental setup followed by results are presented in section 3. Section 4 summarizes the proposed work with feature scope. 2. Methodology This section, describes the details of McNN based text categorization. In the following section, Pre-processing technique used, Feature Selection method followed for Text Representation and Categorization is presented. 2.1. Pre-processing In text documents each word is considered as a feature. But some features are irrelevant and unwanted. Thus, it is necessary to make use pre-processing to eliminate unwanted and irrelevant features/terms. The pre-processing steps like stemming and stop word elimination is applied to do the same. 2.2. Text Representation After pre-processing, text documents are represented using Term Document Matrix (TDM) form. Let us consider that there are N number of documents which belongs to z number of pre-defined classes i.e., C  C1 , C2 , C3 ,..., Cz  . Each class contains n number of documents i.e., D  D1 , D2 , D3 ,..., Dn  with m number of features (terms) F  F1 , F2 , F3 ,..., Fm  . The TDM, Q of dimension N  m is constructed as follows: (1)  Q(a, b) tf ( Fa , Db ) 1  b  N, 1  a  m th th Where, tf (Ta , Db ) is the frequency of a term in the b document. Each entry in the matrix represents the appearance count of the feature in the text document. The text documents represented in TDM form are higher in dimensionality. Thus, to lower the dimensionality in the next step Regularized Locality Preserving Indexing (RLPI) method is employed. 2.3. Feature Selection The higher dimensionality of feature matrix causes reduction in the categorization performance and also maximizes the computational time complexity. It is essential to reduce high dimensionality of feature matrix. Feature selection is mainly used for dimensionality reduction by selecting the most informative features in text documents [1][32]. This process automatically speeds up the learning rate and enhances the performance of categorization. In this paper, to reduce high dimensionality, Regularized Locality Preserving Indexing (RLPI) [4] is employed. RLPI is developed on the basis of Locality Preserving Indexing (LPI). The RLPI identifies two problem in LPI: first one is

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grah embedding problem and second one is regularized least quare problem. This type of alteration averts eigendense matrices decomposition and also significantly reduces the amount of computation time as well as size of memory. The dimension of the RLPI feature matrix R is less than original term document matrix [ N  m]  [ N  j ] , where j presents the number of selected RLPI features and these features will be the input to a classifier. In the next section the text documents are categorized using Meta-cognitive Neural Network. 2.4. Text Categorization To categorize the input text documents, sequential learning Meta-cognitive Neural Network (McNN) is employed. In sequential learning, the training documents arrive one by one and after learning process document is discarded. The McNN is based on Nelson and Narens meta-cognitive model [25], which employs a human like metacognitive learning. Similar to original Nelson and Narens model, McNN has two units: cognitive and meta-cognitive units. The cognitive unit contains a RBF-NN. Meta-cognitive unit comprises a replica of cognitive part along with the learning measures and strategies. When a new text document arrives the meta-cognitive parts calculates the class label and learning measures with the help of cognitive part. Based on learning measures value, it selects appropriate learning strategy for current text document sample. As a result it addresses the human like fundamental learning process: I. what-to-learn, II. when-to-learn, III. how-to-learn. Next subsection presents cognitive, meta-cognitive parts along with learning strategies in detail. 2.4.1. Cognitive unit The cognitive unit contains three layered Radial Basis Function-Neural Network (RBFNN). In RBFNN, input layer maps input document features to hidden layer. The hidden layer adopts a Gaussian activation function whereas output layer adopts linear activation function. RBFNN computes class label for input document as follows: L

 rˆji w jo   w jl l ( Di )

(2)

l 1

Where, j  1, 2,..., N , w jo indicates the bias, w jl indicates the weight between l th hidden layer and j th output neuron, L indicates the number of hidden neurons, l ( Di ) is the response of the l th hidden neuron to the input Di which is computed as:  Di   k 2  l i  (3) l ( D ) exp   k 2      l   Where, lk is the center and lk is the width of the l th Gaussian hidden neuron which describe the feature space R . 2.4.2. Meta-cognitive unit Meta-cognitive unit contains a replica of cognitive units i.e., RBFNN. Meta-cognitive unit computes the following learning measures: Estimated class-label ( Cˆ ): The estimated class label is computed based on predicted output Cˆ  max(rˆji )

Maximum Hinge Error (  ): The maximum hinge error MHE is computed as:   max  j

rˆji as follows: (4)

(5)



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o if rˆji rji  1  Where,  j  i i  rˆj  rj otherwise

Confidence of classifier (  ( j | Di ) ): The confidence of a classifier is computed as: min(1, max(1, rˆji ))  1  ( j | Di )  2

(6)

Class-wise significance (  C ): Class-wise significance gives the class-wise distribution and it is computed as follows: C 1 L C  C   ( Di , lC ) (7) L l 1 Where, LC indicates the number of neurons associated with class C and lc is the center of the l th Gaussian hidden neuron associated with class C . 2.4.3. Learning Strategies Based on the aforementioned learning measures meta-cognitive unit chooses appropriate learning strategies. These learning strategies address the basic human learning principles i.e., what-to-learn, when-to-learn and how-tolearn. By adopting one of the following strategies meta-cognitive unit manages the sequential learning process. Document delete strategy: This strategy avoids the over-training by preventing the learning of similar documents. When new training document contains the same knowledge information in the cognitive unit (i.e., predicted and actual labels are same) then, the input document is deleted. The document delete criterion is given as: (8)  ( j | Di )   d AND C   Cˆ Where,  d indicates the meta-cognitive deletion threshold. Neuron growth strategy: When the input training document contains a significant information and estimated class label is different from actual label then a new hidden neuron is added to capture the knowledge. This strategy criterion is given by (9) Cˆ  C AND C ( Di )  c AND    a Where,  c indicates a meta-cognitive knowledge measurement threshold,  a indicates self adaptive meta-cognitive addition threshold. Network parameter update strategy: The cognitive unit parameters such as weights between input layer, hidden layer and output layer, center and width of Gaussian hidden neurons are updated, if following criterion is satisfied. (10) Cˆ  C AND   u Where,  u indicates self adaptive meta-cognitive update threshold. Document reserve strategy: When new training document fails to satisfy aforementioned criterias, then the document is reserved for future learning. In McNN, the aforementioned learning strategies address the human like meta-cognitive learning process. Document delete strategy addresses the what-to-learn, neuron growth strategy and parameter update strategy address how-to-learn, document reserve strategy addresses when-to-learn process.

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In McNN, the training process stops when no further document is available. In testing process, the class label of text document is predicted using McNN trained model. The Algorithm 1 presents the individual steps involved to categorize text documents using McNN. Algorithm 1: McNN Classifier Data: N number of documents, z number of pre-defined classes ( C ), each class contains n number of documents ( D ) with m number of features F , knowledge threshold (  c ), addition threshold (  a ), delete threshold (  d ) and update threshold (  u ) Result: Class Label Step 1: Pre-process and represent text documents in TDM form Step 2: Select feature using RLPI repeat Step 3: Cognitive unit computes the output of each input document using equation (2) Step 4: Meta-cognitive unit computes Estimated class-label ( Cˆ ), Maximum Hinge Error (  ), Confidence (  ( j | Di ) ) and Class-wise significance (  C ) using equations: 4, 5, 6 and 7 Step 5: Adopt one of the strategies if(  ( j | Di )   d AND C   Cˆ ) Then, delete document else if( Cˆ  C AND C ( Di )  c AND    a ) Then, allocate new hidden neuron growth else if( Cˆ  C AND   u ) Then, update weights, center and width else Document is in reserve state and later used it for learning until ( N  0) is satisfied 3. Experiments 3.1. Dataset In order to assess the effectiveness of the Meta-cognitive Neural Network classifier, experiments are conducted on Reuters-21578 standard benchmark datasets [Reuters-21578()]. Reuters-21578 is one of the popular dataset for text categorization, which consists of 21,578 documents with pre-defined 135 different classes and these documents are not equal proposition for each class. For the smooth conduction of experiments, ten largest classes are selected and documents contains multiple class labels are discarded. The leave one out cross validation technique is used to assess the performance of the classifiers. 3.2. Evaluation Metric To assess the performance of the proposed framework, Leave One Out Cross Validation (LOOCV) method is adopted. LOOCV is degenerate case of k-fold cross validation technique. In LOOCV, the true error rate is computed as the average error rate of test documents and it is computed as: 1 if Cˆ  C 1 N  (11) R  Ri Ri    N i 1  0 otherwise Where N indicates the total number of documents R is the average error rate and Ri is the true error rate of i th document.



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3.3. Experimental setup and results The performance of the proposed framework is compared against two widely used neural network based classifiers viz., MLP and RBF-NN. In the proposed framework the selection of different threshold value plays a vital role. The knowledge contribution of the current document is identified using knowledge threshold (  c ) and it depends on spherical potential. The value of spherical potential varies from 0 to 1. When the current document contains the information similar to knowledge, then the value of spherical potential is near to 1. On the other hand, lower value of spherical potential refers to higher novelty of the document. If  c value is set closer to zero then the network restricts the addition of neurons. If  c value is set closer to 1, then all the documents are recognized as novel documents. In this paper,  c value is varied from 0.4 to 0.8. The delete threshold  d controls the over training. If  d value is set as 0.5, then more documents get deleted without learning. If  d value is set as 1, then it causes the over training. In this paper,  d value is varied between 0.85 to 0.95. The addition threshold  a controls the addition of the neurons. If  a close to 1, then all misclassified documents are used to addition of neuron. If  a value is set as 2 then only few neuron will be added. In this paper,  a value is varied between 1 to 2. The update threshold  u controls the parameter updates. If  u value is set as 1, then no documents will be used in updating. If  u value is set as 0, then all documents will be used for updating. In this paper,  u value is varied between 0.3 to 0.7. After conduction of several rounds of experiments, it is found that the proposed framework gives best categorization results for the above mentioned threshold values. Table 1. True Error Rate achieved using different framework. Framework

True Error Rate

Number of Documents used for Learning

MLP

0.419

9142

RBF-NN

0.372

9142

McNN

0.193

6218

Table 1 presents the comparison of true error rate and the number of documents used for learning in proposed framework with MLP and RBFNN. MLP and RBFNN uses 9142 text documents for learning, where as the proposed method uses only 6218 documents. In addition, MLP and RBFNN uses fixed number of hidden neurons, where as proposed method dynamically adds new hidden neuron based on the learning strategy. From Table 1 it is infer that, the proposed framework has less true error rate and uses the less documents than other two neural network based methods. The main advantage of the proposed framework is that it adapts the learning strategies appropriately. Unlike in the other neural network based methods where it uses the entire document set for learning, the proposed meta-cognitive neural network uses only distinct documents. 4. Conclusion In this paper, a sequential learning framework for text categorization by employing Meta-cognitive Neural Network (McNN) is presented. Unlike in other neural network methods, where all the training documents are used for learning, McNN adapts the appropriate learning strategies which help to decide what-to-learn, when-to-learn and how-to-learn efficiently. The experiments are conducted on the standard Reuters-21578 dataset and results are compared with well-known neural network classifiers like MLP and RBFNN. The performance of the McNN is evaluated in terms of true error rate. Since the proposed framework adapts the self-regularity learning of human being i.e., what to learn, when to learn and how to learn, it uses less number of training documents than other two NN models. From the experimental results, it is infer that the proposed framework gives better categorization results compared against other two NN classifiers. The performance of the proposed method mainly depends on the threshold values and selecting the these value is a very tedious job. On the other hand, a major problem with text

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categorization is the high dimensionality of the feature space. In Future, different feature selection methods will be employed to reduce the dimensionality which intern improves the performance of text categorization. References [1] Agnihotri, D. Verma, K. and Tripathi, P. (2017) “Variable Global Feature Selection Scheme for automatic classification of text documents.” Expert Systems with Applications, 81: 268-281. [2] Allahyari, M. Pouriyeh, S. Assefi, M. Safaei, S. Trippe, E.D. Gutierrez, J.B. and Kochut, K. (2017) “A brief survey of text mining: Classification, clustering and extraction techniques.” arXiv preprint arXiv:1707.02919. [3] Babu, G.S. and Suresh, S. (2012) “Meta-cognitive neural network for classification problems in a sequential learning framework.” Neurocomputing, 81: 86-96. [4] Cai, D. He, X. Zhang, W.V. and Han, J. (2007) “Regularized locality preserving indexing via spectral regression.” In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, 741-750. [5] Chen, G. Ye, D. Xing, Z. Chen, J. and Cambria, E. (2017) “Ensemble application of convolutional and recurrent neural networks for multi-label text categorization.” 2017 International Joint Conference on Neural Networks (IJCNN), 2377-2383. [6] Diab, D.M. and El Hindi, K.M. (2017) “Using differential evolution for fine tuning naïve Bayesian classifiers and its applica tion for text classification.” Applied Soft Computing, 54: 183-199. [7] Guru, D.S. Harish, B.S. and Manjunath, S. (2010) “Symbolic representation of text documents.” In Proceedings of the Third Annual ACM Bangalore Conference, 18. [8] Guru, D.S. Suhil, M. Raju, L.N. and Kumar, V. (2018) “An Alternative Framework for Univariate Filter based Feature Selection for Text Categorization.” Pattern Recognition Letters. [9] Harish, B.S. and Revanasiddappa, M.B. (2017) “A Comprehensive Survey on various Feature Selection Methods to Categorize Text Documents.” International Journal of Computer Applications, 164(8). [10] Harish, B.S. Revanasiddappa, M.B. and Kumar, S.A. (2015) “Symbolic Representation of Text Documents Using Multiple Kernel FCM.” International Conference on Mining Intelligence and Knowledge Exploration, 93-102. [11] Harish, B.S. Guru, D.S. and Manjunath, S. (2010) “Representation and classification of text documents: A brief review.” IJCA, Special Issue on RTIPPR (2): 110-119. [12] Huang, G.B. Saratchandran, P. and Sundararajan, N. (2004) “An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks.” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(6): 2284-2292. [13] Jiang, E. (2007) “Detecting spam email by radial basis function networks.” International journal of knowledge-based and intelligent engineering systems, 11(6): 409-418. [14] Jiang, E.P. (2009a) “Semi-supervised text classification using RBF networks.” In International Symposium on Intelligent Data Analysis, 95-106. [15] Jiang, E.P. (2009b) “Semi-supervised text classification using RBF networks.” In International Symposium on Intelligent Data Analysis, 95-106. [16] Jiang, S. Pang, G. Wu, M. and Kuang, L. (2012) “An improved K-nearest-neighbor algorithm for text categorization.” Expert Systems with Applications, 39(1): 1503-1509. [17] Jo, T. (2010) “NTC (Neural Text Categorizer): Neural network for text categorization.” International Journal of Information Studies, 2(2): 83-96. [18] Joachims, T. (1998) “Text categorization with support vector machines: Learning with many relevant features.” Machine Learning: ECML-98, 137-142. [19] Kang, M. Ahn, J. and Lee, K. (2018) “Opinion mining using ensemble text hidden Markov models for text classification.” Expert Systems with Applications, 94: 218-227. [20] Kaufmann, P. (2016) “Supervised Learning With Complex Valued Neural Networks.” Neural Networks, 1: 2. [21] Labani, M. Moradi, P. Ahmadizar, F. and Jalili, M. (2018) “A novel multivariate filter method for feature selection in text classification problems.” Engineering Applications of Artificial Intelligence, 70: 25-37. [22] Li, C.H. and Park, S.C. (2006) “Text categorization based on artificial neural networks.” In International conference on neural information processing, 302-311. [23] Liu, C. Wang, W. Tu, G. Xiang, Y. Wang, S. and Lv, F. (2017) “A new Centroid-Based Classification model for text categorization.” Knowledge-Based Systems, 136: 15-26. [24] Motwani, M. Tiwari, A. and Sharma, S. (2015) “Investigation of BPNN & RBFN in text classification b y Active search.” In 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 1-6. [25] Nelson, T.O. (1990) “Metamemory: A theoretical framework and new findings.” In Psychology of learning and motivation, 26: 125-173. [26] Ng, H.T. Goh, W.B. and Low, K.L. (1997) “Feature selection, perceptron learning, and a usability case study for text categorization.” In ACM SIGIR Forum, 31: 67-73.



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[27] Orr, M.J. (1996) “Introduction to radial basis function networks.” [28] Pereira, R.B. Plastino, A. Zadrozny, B. and Merschmann, L.H. (2018) “Categorizing feature selection methods for multi-label classification.” Artificial Intelligence Review, 49(1): 57-78. [29] Pratama, M. Lu, J. Anavatti, S. Lughofer, E. and Lim, C.P. (2016) “An incremental meta-cognitive-based scaffolding fuzzy neural network.” Neurocomputing, 171: 89-105. [30] Priya, R.V. Sethunadh, R. Ramdas, V. and Kumar, G.S. (2017) “Measurement error compensation using metacognitive elm based artificial neural network.” 2017 International Conference on Networks & Advances in Computational Technologies (NetACT), 447-452. [31] Ramasundaram, S. and Victor, S.P. (2010) “Text categorization by backpropagation network.” International Journal of Computer Applications, 8(6): 1-5. [32] Rehman, A. Javed, K. and Babri, H.A. (2017) “Feature selection based on a normalized difference measure for text classification.” Information Processing & Management, 53(2): 473-489 [33] Reuters-21578, http://www.daviddlewis.com/resources/testcollections/reuters21578/ [34] Revanasiddappa, M.B. Harish, B.S. and Manjunath, S. (2014) “Document classification using symbolic classifiers.” 2014 International Conference on Contemporary Computing and Informatics (IC3I), 299-303. [35] Runxuan, Z. (2005) “Efficient sequential and batch learning artificial neural network methods for classification problems.” 2: 825-845. [36] Sebastiani, F. (2002) “Machine learning in automated text categorization.” ACM computing surveys (CSUR), 34 (1): 1-47. [37] Skovajsova, L. (2010) “Text document retrieval by feed-forward neural networks.” Information Sciences and Technologies Bulletin of the ACM Slovakia, 2(2): 70-78. [38] Suresh, S. Dong, K. and Kim, H.J. (2010) “A sequential learning algorithm for self-adaptive resource allocation network classifier.” Neurocomputing, 73(16-18): 3012-3019. [39] Yu, L. Wang, S. and Lai, K. K. (2010) “Foreign-exchange-rate forecasting with artificial neural networks.” Springer Science & Business Media, 107. [40] Zhang, Y. and Er, M.J. (2016) “Sequential active learning using meta-cognitive extreme learning machine.” Neurocomputing, 173: 835-844.