Classification and Feature Selection Techniques in Data Mining

Data mining is a form of knowledge discovery essential for solving problems in a specific domain. Classification is a technique used for discovering classes of unknown data. Various methods for classification exists like bayesian, decision trees, rule based, neural networks etc. Before applying any mining technique, irrelevant attributes needs to be filtered. Filtering is done using different feature selection techniques like wrapper, filter, embedded technique. This paper is an introductory paper on different techniques used for classification and feature selection.

[1]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[2]  Anil K. Jain,et al.  Dimensionality reduction using genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[3]  Gisele L. Pappa,et al.  Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach , 2009 .

[4]  Jason Catlett,et al.  On Changing Continuous Attributes into Ordered Discrete Attributes , 1991, EWSL.

[5]  Jerry M. Mendel,et al.  Type-2 fuzzy logic systems , 1999, IEEE Trans. Fuzzy Syst..

[6]  P. Gaur.  Neural networks in data mining , 2018 .

[7]  Evangelos Simoudis,et al.  Integrating Inductive and Deductive Reasoning for Data Mining , 1996, Advances in Knowledge Discovery and Data Mining.

[8]  Gisele L. Pappa,et al.  Automating the Design of Data Mining Algorithms , 2010 .

[9]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[10]  Yashpal Singh,et al.  NEURAL NETWORKS IN DATA MINING , 2014 .

[11]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[12]  Chitta Baral,et al.  Fuzzy C-means Clustering with Prior Biological Knowledge , 2022 .

[13]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[14]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[15]  Dorian Pyle,et al.  Data Preparation for Data Mining , 1999 .

[16]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

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

[18]  G. Cooper,et al.  An efficient bayesian method for predicting clinical outcomes from genome-wide data. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[19]  Vojislav Kecman.  Fuzzy Logic Systems , 2001 .

[20]  Adnan Darwiche,et al.  Modeling and Reasoning with Bayesian Networks , 2009 .

[21]  Toshinori Munakata,et al.  Knowledge discovery , 1999, Commun. ACM.

[22]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[23]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[24]  Walter Daelemans,et al.  Combined Optimization of Feature Selection and Algorithm Parameter Interaction in Machine Learning of Language , 2003 .

[25]  Kyuseok Shim,et al.  Building Decision Trees with Constraints , 2001 .

[26]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[27]  Oscar Castillo,et al.  Interval Type-2 Fuzzy Logic Toolbox , 2007, Eng. Lett..

[28]  Isabelle Guyon,et al.  Discovering Informative Patterns and Data Cleaning , 1996, Advances in Knowledge Discovery and Data Mining.

[29]  Tan Jun-shan,et al.  Application of Genetic Algorithm in Data Mining , 2009, 2009 First International Workshop on Education Technology and Computer Science.

[30]  Frank Hsu,et al.  Knowledge Discovery , 2014, Encyclopedia of Social Network Analysis and Mining.