A New Fuzzy Stacked Generalization Technique and Analysis of its Performance

TL;DR

This paper introduces Fuzzy Stacked Generalization (FSG), a novel ensemble method that combines fuzzy k-NN classifiers with a hierarchical distance learning strategy to improve classification accuracy, outperforming existing ensemble techniques.

Contribution

The paper proposes a new Fuzzy Stacked Generalization technique utilizing a hierarchical distance learning strategy and ensemble of fuzzy k-NN classifiers, demonstrating superior performance over state-of-the-art methods.

Findings

FSG outperforms Adaboost, Random Subspace, and Rotation Forest on multiple datasets.

Diversity and cooperation among classifiers enhance ensemble performance.

Weak classifiers can significantly boost overall accuracy when properly integrated.

Abstract

In this study, a new Stacked Generalization technique called Fuzzy Stacked Generalization (FSG) is proposed to minimize the difference between N -sample and large-sample classification error of the Nearest Neighbor classifier. The proposed FSG employs a new hierarchical distance learning strategy to minimize the error difference. For this purpose, we first construct an ensemble of base-layer fuzzy k- Nearest Neighbor (k-NN) classifiers, each of which receives a different feature set extracted from the same sample set. The fuzzy membership values computed at the decision space of each fuzzy k-NN classifier are concatenated to form the feature vectors of a fusion space. Finally, the feature vectors are fed to a meta-layer classifier to learn the degree of accuracy of the decisions of the base-layer classifiers for meta-layer classification. Rather than the power of the individual base layer-classifiers, diversity and cooperation of the classifiers become an important issue to improve the overall performance of the proposed FSG. A weak base-layer classifier may boost the overall performance more than a strong classifier, if it is capable of recognizing the samples, which are not recognized by the rest of the classifiers, in its own feature space. The experiments explore the type of the collaboration among the individual classifiers required for an improved performance of the suggested architecture. Experiments on multiple feature real-world datasets show that the proposed FSG performs better than the state of the art ensemble learning algorithms such as Adaboost, Random Subspace and Rotation Forest. On the other hand, compatible performances are observed in the experiments on single feature multi-attribute datasets.