Abstract
One of the crucial problems of designing a classifier ensemble is the proper choice of the base classifier line-up. Basically, such an ensemble is formed on the basis of individual classifiers, which are trained in such a way to ensure their high diversity or they are chosen on the basis of pruning which reduces the number of predictive models in order to improve efficiency and predictive performance of the ensemble. This work is focusing on clustering-based ensemble pruning, which looks for the group of similar classifiers which are replaced by their representatives. We propose a novel pruning criterion based on well-known diversity measures and describe three algorithms using classifier clustering. The first method selects the model with the best predictive performance from each cluster to form the final ensemble, the second one employs the multistage organization, where instead of removing the classifiers from the ensemble each classifier cluster makes the decision independently, while the third proposition combines multistage organization and sampling with replacement. The proposed approaches were evaluated using 30 datasets with different characteristics. Experimentation results validated through statistical tests confirmed the usefulness of the proposed approaches.
Highlights
Ensemble methods have been a well-known and quickly developing area of research
This work addresses the topic of classifier ensemble pruning, especially clustering-based ensemble pruning methods, in which our goal is to decrease the total number of ensemble members
The main aim of this work was to propose a novel, effective classifier pruning method based on clustering
Summary
Ensemble methods have been a well-known and quickly developing area of research. They owe their success to the fact that their application allows for dealing with a variety of learning problems, such as learning from distributed data sources [23], improving overall classification accuracy [28], learning from data streams [18], hyperspectral image analysis [17] and imbalanced data classification [19]. While in the classic approach only one learner is trained for a given problem, ensemble methods construct many classifiers based on the available training data and combine them to obtain a final decision. It is worth mentioning that [13] enumerated two main approaches to design a classifier ensemble, i.e., coverage optimization, where the combination rule is given and the main effort is to form an appropriate line-up of individual predictors, and decision optimization which aims for finding an optimal combination rule, while the ensemble line-up is fixed
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