Learning using Heterogeneous Classifier in Data Mining

Amit Thakkar; Reshma Idresh Lakhani; Amit Ganatra

Learning using Heterogeneous Classifier in Data Mining

Amit Thakkar, Reshma Idresh Lakhani, Amit Ganatra

Abstract

Data Mining can be considered an analytic process designed to explore business or market data to search for consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. Data mining is useful for prediction. We can improve accuracy of different classifiers by combining various classifiers and taking their predictions. One such method is Stacking, an ensemble method in which a number of base classifiers are combined using one meta-classifier which learns their outputs. This enhances the benefits obtained by individual classifiers. This paper is a review work of different approaches proposed by various authors in their paper.

Keywords

Ensemble of Classifiers, Bagging, Boosting, Staking, Troika.

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References

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Combining Distributed Classifies by Stacking - WEI Yanyan, LI Taoshen, GE Zhihui

Troika-An Improved Stacking Schema for Classification Tasks- Eitan Menahem, Lior Rokach, Yuval Elovici

Data Mining, Concepts and Techniques, Jiawei Han and Micheline Kamber

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