Rough set Based Ensemble Classifier forWeb Page Classification

Authors:
Suman Saha;C.A. Murthy;Sankar K. Pal
Affiliations:
Center for Soft Computing Research, Indian Statistical Institute, India. E-mail: {ssaha_r,murthy,sankar}@isical.ac.in;Center for Soft Computing Research, Indian Statistical Institute, India. E-mail: {ssaha_r,murthy,sankar}@isical.ac.in;Center for Soft Computing Research, Indian Statistical Institute, India. E-mail: {ssaha_r,murthy,sankar}@isical.ac.in
Venue:
Fundamenta Informaticae
Year:
2007

Citing 18
Cited 2

A comparison of classifiers and document representations for the routing problem

SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Bagging predictors

Machine Learning
Combining classifiers in text categorization

SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Machine Learning
Rough set algorithms in classification problem

Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Using Correspondence Analysis to Combine Classifiers

Machine Learning
Combining Classifiers by Constructive Induction

ECML '98 Proceedings of the 10th European Conference on Machine Learning
Feature Engineering for Text Classification

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Rough Sets and Decision Algorithms

RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Fast and accurate text classification via multiple linear discriminant projections

The VLDB Journal — The International Journal on Very Large Data Bases
Is Combining Classifiers with Stacking Better than Selecting the Best One?

Machine Learning
Multi-Stage Classification

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Solving multiclass learning problems via error-correcting output codes

Journal of Artificial Intelligence Research
Issues in stacked generalization

Journal of Artificial Intelligence Research
Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing

RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Bagging, boosting, and C4.S

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1

Autonomous Knowledge-oriented Clustering Using Decision-Theoretic Rough Set Theory

Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
Iterative meta-clustering through granular hierarchy of supermarket customers and products

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.