# A Novel Multiple Classifier Generation and Combination Framework Based on Fuzzy Clustering and Individualized Ensemble Construction

## TL;DR

This paper introduces ICE, a new individualized ensemble method that groups training data into overlapping clusters, builds classifiers for each, and predicts test instances by leveraging the most similar training instances, improving classification stability.

## Contribution

The paper presents a novel framework combining fuzzy clustering and individualized ensemble construction, enhancing classifier robustness and adaptability across diverse datasets.

## Key findings

- ICE outperforms existing MCS methods on many benchmarks.
- It demonstrates stable improvements across 49 datasets.
- The approach is versatile and easily integrable with various models.

## Abstract

Multiple classifier system (MCS) has become a successful alternative for improving classification performance. However, studies have shown inconsistent results for different MCSs, and it is often difficult to predict which MCS algorithm works the best on a particular problem. We believe that the two crucial steps of MCS - base classifier generation and multiple classifier combination, need to be designed coordinately to produce robust results. In this work, we show that for different testing instances, better classifiers may be trained from different subdomains of training instances including, for example, neighboring instances of the testing instance, or even instances far away from the testing instance. To utilize this intuition, we propose Individualized Classifier Ensemble (ICE). ICE groups training data into overlapping clusters, builds a classifier for each cluster, and then associates each training instance to the top-performing models while taking into account model types and frequency. In testing, ICE finds the k most similar training instances for a testing instance, then predicts class label of the testing instance by averaging the prediction from models associated with these training instances. Evaluation results on 49 benchmarks show that ICE has a stable improvement on a significant proportion of datasets over existing MCS methods. ICE provides a novel choice of utilizing internal patterns among instances to improve classification, and can be easily combined with various classification models and applied to many application domains.