Rough set Based Ensemble Classifier forWeb Page Classification

Combining the results of a number of individually trained classification systems to obtain a more accurate classifier is a widely used technique in pattern recognition. In this article, we have introduced a rough set based meta classifier to classify web pages. The proposed method consists of two parts. In the first part, the output of every individual classifier is considered for constructing a decision table. In the second part, rough set attribute reduction and rule generation processes are used on the decision table to construct a meta classifier. It has been shown that (1) the performance of the meta classifier is better than the performance of every constituent classifier and, (2) the meta classifier is optimal with respect to a quality measure defined in the article. Experimental studies show that the meta classifier improves accuracy of classification uniformly over some benchmark corpora and beats other ensemble approaches in accuracy by a decisive margin, thus demonstrating the theoretical results. Apart from this, it reduces the CPU load compared to other ensemble classification techniques by removing redundant classifiers from the combination.

[1]  J. Ross Quinlan,et al.  Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.

[2]  Shourya Roy,et al.  Fast and accurate text classification via multiple linear discriminant projections , 2003, The VLDB Journal.

[3]  Christopher J. Merz,et al.  Using Correspondence Analysis to Combine Classifiers , 1999, Machine Learning.

[4]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[5]  Grigorios Tsoumakas,et al.  Effective Voting of Heterogeneous Classifiers , 2004, ECML.

[6]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[7]  Zdzisław Pawlak,et al.  Rough Sets And Decision Analysis , 2000 .

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

[9]  João Gama,et al.  Combining Classifiers by Constructive Induction , 1998, ECML.

[10]  Zdzislaw Pawlak,et al.  Rough Sets and Decision Algorithms , 2000, Rough Sets and Current Trends in Computing.

[11]  Ian H. Witten,et al.  Issues in Stacked Generalization , 2011, J. Artif. Intell. Res..

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

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

[14]  Stan Matwin,et al.  Feature Engineering for Text Classification , 1999, ICML.

[15]  Anil K. Jain,et al.  Classification of text documents , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[16]  G DietterichThomas.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees , 2000 .

[17]  Bernard Zenko,et al.  Is Combining Classifiers with Stacking Better than Selecting the Best One? , 2004, Machine Learning.

[18]  Hinrich Schütze,et al.  A comparison of classifiers and document representations for the routing problem , 1995, SIGIR '95.

[19]  Ted E. Senator,et al.  Multi-stage classification , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[20]  Bernard Zenko,et al.  A comparison of stacking with meta decision trees to bagging, boosting, and stacking with other methods , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[21]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[22]  Kagan Tumer,et al.  Robust Combining of Disparate Classifiers through Order Statistics , 1999, Pattern Analysis & Applications.