A NEW CLASSIFICATION TECHNIQUE BASED ON HYBRID FUZZY SOFT SET THEORY AND SUPERVISED FUZZY C-MEANS

[1]  Pabitra Kumar Maji,et al.  FUZZY SOFT SETS , 2001 .

[2]  Tipu Z. Aziz,et al.  Parkinson's Disease tremor classification - A comparison between Support Vector Machines and neural networks , 2012, Expert Syst. Appl..

[3]  R. Morelos-Zaragoza.  The art of error correcting coding , 2002 .

[4]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[5]  Liqun Gao,et al.  Letter to the editor: Comment on A fuzzy soft set theoretic approach to decision making problems , 2009 .

[6]  Piyush Kumar,et al.  Fast construction of k-nearest neighbor graphs for point clouds , 2010, IEEE Transactions on Visualization and Computer Graphics.

[7]  Herbert A. Simon,et al.  Applications of machine learning and rule induction , 1995, CACM.

[8]  Tutut Herawan,et al.  A novel soft set approach in selecting clustering attribute , 2012, Knowl. Based Syst..

[9]  Naim Çagman,et al.  Soft set theory and uni-int decision making , 2010, Eur. J. Oper. Res..

[10]  Boris Mirkin,et al.  Data analysis, mathematical statistics, machine learning, data mining: Similarities and differences , 2011, 2011 International Conference on Advanced Computer Science and Information Systems.

[11]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[12]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[13]  S S Stevens,et al.  On the Theory of Scales of Measurement. , 1946, Science.

[14]  Jeng-Shyang Pan,et al.  Fast k-nearest neighbor search algorithm based on pyramid structure of wavelet transform and its application to texture classification , 2010, Digit. Signal Process..

[15]  Desheng Dash Wu,et al.  Using text mining and sentiment analysis for online forums hotspot detection and forecast , 2010, Decis. Support Syst..

[16]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[17]  Vijay V. Raghavan,et al.  A critical analysis of vector space model for information retrieval , 1986 .

[18]  Melody Mauldin Covington,et al.  Dictionary of Computer and Internet Terms , 1998 .

[19]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[20]  Naim Çagman,et al.  Soft sets and soft groups , 2007, Inf. Sci..

[21]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[22]  Sarah Jane Delany.  k-Nearest Neighbour Classifiers , 2007 .

[23]  David L. Olson,et al.  Advanced Data Mining Techniques , 2008 .

[24]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[25]  Daphne Koller,et al.  Hierarchically Classifying Documents Using Very Few Words , 1997, ICML.

[26]  D. Molodtsov.  Soft set theory—First results , 1999 .

[27]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[28]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[29]  A. R. Roy,et al.  An application of soft sets in a decision making problem , 2002 .

[30]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[31]  Rong Hu,et al.  Active Learning for Text Classification , 2011 .

[32]  Jianhua Guo,et al.  A Bayesian feature selection paradigm for text classification , 2012, Inf. Process. Manag..

[33]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[34]  Karl-Michael Schneider.  On Word Frequency Information and Negative Evidence in Naive Bayes Text Classification , 2004, EsTAL.

[35]  David R. Karger,et al.  Tackling the Poor Assumptions of Naive Bayes Text Classifiers , 2003, ICML.

[36]  Ryan M. Rifkin,et al.  In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..

[37]  Timothy A. Gonsalves,et al.  Feature Selection for Text Classification Based on Gini Coefficient of Inequality , 2010, FSDM.

[38]  Ian Witten,et al.  Data Mining , 2000 .

[39]  Mohamed S. Kamel,et al.  Pairwise optimized Rocchio algorithm for text categorization , 2011, Pattern Recognit. Lett..

[40]  Grigorios Tsoumakas,et al.  Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.

[41]  Daowu Pei,et al.  From soft sets to information systems , 2005, 2005 IEEE International Conference on Granular Computing.

[42]  Susan T. Dumais,et al.  Inductive learning algorithms and representations for text categorization , 1998, CIKM '98.

[43]  Hae-Chang Rim,et al.  Some Effective Techniques for Naive Bayes Text Classification , 2006, IEEE Transactions on Knowledge and Data Engineering.

[44]  David D. Lewis,et al.  Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.

[45]  Karl-Michael Schneider,et al.  A Comparison of Event Models for Naive Bayes Anti-Spam E-Mail Filtering , 2003, EACL.

[46]  Tutut Herawan,et al.  A new efficient normal parameter reduction algorithm of soft sets , 2011, Comput. Math. Appl..

[47]  Mustafa Mat Deris,et al.  A soft set approach for association rules mining , 2011, Knowl. Based Syst..

[48]  Rudy Prabowo,et al.  Sentiment analysis: A combined approach , 2009, J. Informetrics.

[49]  Michael A. Shepherd,et al.  Support vector machines for text categorization , 2003, 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the.

[50]  Eric C. C. Tsang,et al.  The parameterization reduction of soft sets and its applications , 2005 .

[51]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[52]  Xiaoyan Liu,et al.  On some new operations in soft set theory , 2009, Comput. Math. Appl..

[53]  Naim Çagman,et al.  Soft matrix theory and its decision making , 2010, Comput. Math. Appl..

[54]  Jing-Yu Yang,et al.  Generalization of Soft Set Theory: From Crisp to Fuzzy Case , 2007, ICFIE.

[55]  Young Bae Jun,et al.  An adjustable approach to fuzzy soft set based decision making , 2010, J. Comput. Appl. Math..

[56]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[57]  Yan Zou,et al.  Data analysis approaches of soft sets under incomplete information , 2008, Knowl. Based Syst..

[58]  Jiawei Han,et al.  Classifying large data sets using SVMs with hierarchical clusters , 2003, KDD '03.

[59]  Grigorios Tsoumakas,et al.  Multi-Label Classification: An Overview , 2007, Int. J. Data Warehous. Min..

[60]  Bernhard Schölkopf,et al.  Shrinking the Tube: A New Support Vector Regression Algorithm , 1998, NIPS.

[61]  Radim Sára,et al.  A Weak Structure Model for Regular Pattern Recognition Applied to Facade Images , 2010, ACCV.

[62]  Feng Feng,et al.  Generalized uni-int decision making schemes based on choice value soft sets , 2012, Eur. J. Oper. Res..

[63]  Pat Langley,et al.  An Analysis of Bayesian Classifiers , 1992, AAAI.

[64]  Pinaki Majumdar,et al.  Generalised fuzzy soft sets , 2010, Comput. Math. Appl..

[65]  Kate Smith-Miles,et al.  On learning algorithm selection for classification , 2006, Appl. Soft Comput..

[66]  Guy W. Mineau,et al.  A simple KNN algorithm for text categorization , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[67]  Irina Rish,et al.  An empirical study of the naive Bayes classifier , 2001 .

[68]  S. K. Samanta,et al.  SIMILARITY MEASURE OF SOFT SETS , 2008 .

[69]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[70]  W. Bruce Croft,et al.  Combining classifiers in text categorization , 1996, SIGIR '96.

[71]  Ajoy Kumar Ray,et al.  Texture Classification Using a Novel, Soft-Set Theory Based Classification Algorithm , 2006, ACCV.

[72]  Susan T. Dumais,et al.  A Bayesian Approach to Filtering Junk E-Mail , 1998, AAAI 1998.

[73]  Carlos Ordonez,et al.  Bayesian Classifiers Programmed in SQL , 2010, IEEE Transactions on Knowledge and Data Engineering.

[74]  Shengyi Jiang,et al.  An improved K-nearest-neighbor algorithm for text categorization , 2012, Expert Syst. Appl..

[75]  Miguel Rio,et al.  Symbiotic filtering for spam email detection , 2011, Expert Syst. Appl..

[76]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[77]  Dino Isa,et al.  A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine , 2012, Expert Syst. Appl..

[78]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[79]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[80]  Yiming Yang,et al.  A re-examination of text categorization methods , 1999, SIGIR '99.

[81]  A. R. Roy,et al.  Soft set theory , 2003 .

[82]  Luis M. de Campos,et al.  Bayesian network models for hierarchical text classification from a thesaurus , 2009, Int. J. Approx. Reason..

[83]  Steven Li,et al.  The normal parameter reduction of soft sets and its algorithm , 2008, Comput. Math. Appl..

[84]  Houkuan Huang,et al.  Feature selection for text classification with Naïve Bayes , 2009, Expert Syst. Appl..

[85]  Zhi Xiao,et al.  A combined forecasting approach based on fuzzy soft sets , 2009 .

[86]  Ahmad Nazari Mohd Rose,et al.  Soft Set Theoretic Approach for Dimensionality Reduction , 2009, FGIT-DTA.

[87]  Isabelle Guyon,et al.  Comparison of classifier methods: a case study in handwritten digit recognition , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[88]  Sean Luke,et al.  Evolving kernels for support vector machine classification , 2007, GECCO '07.

[89]  K. A. Vidhya,et al.  A Survey of Naïve Bayes Machine Learning approach in Text Document Classification , 2010, ArXiv.

[90]  Florentino Fernández Riverola,et al.  Rough sets for spam filtering: Selecting appropriate decision rules for boundary e-mail classification , 2012, Appl. Soft Comput..

[91]  Michel Barlaud,et al.  Fast k nearest neighbor search using GPU , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[92]  J. J. Rocchio,et al.  Relevance feedback in information retrieval , 1971 .

[93]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[94]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[95]  Yoram Singer,et al.  Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers , 2000, J. Mach. Learn. Res..

[96]  Songbo Tan,et al.  Neighbor-weighted K-nearest neighbor for unbalanced text corpus , 2005, Expert Syst. Appl..

[97]  Tutut Herawan,et al.  DFIS: A novel data filling approach for an incomplete soft set , 2012, Int. J. Appl. Math. Comput. Sci..

[98]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[99]  A. R. Roy,et al.  A fuzzy soft set theoretic approach to decision making problems , 2007 .

[100]  Saso Dzeroski,et al.  Two stage architecture for multi-label learning , 2012, Pattern Recognit..

[101]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[102]  Peter Willett,et al.  Readings in information retrieval , 1997 .

[103]  Klaus Hechenbichler,et al.  Weighted k-Nearest-Neighbor Techniques and Ordinal Classification , 2004 .

[104]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[105]  Shin Ishii,et al.  Optimal Aggregation of Binary Classifiers for Multiclass Cancer Diagnosis Using Gene Expression Profiles , 2009, TCBB.

[106]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[107]  David Madigan,et al.  On the Naive Bayes Model for Text Categorization , 2003, AISTATS.

[108]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .