Aggregation of Classifiers: A Justifiable Information Granularity Approach
This work addresses the challenge of improving ensemble classifier performance for machine learning practitioners, though it appears incremental as it builds on existing ensemble methods with a novel granularity-based technique.
The study tackled the problem of combining multiple classifiers in an ensemble system by introducing a new approach using interval membership values based on information granules, which outperformed benchmark algorithms like AdaBoost and Bagging on UCI datasets.
In this study, we introduce a new approach to combine multi-classifiers in an ensemble system. Instead of using numeric membership values encountered in fixed combining rules, we construct interval membership values associated with each class prediction at the level of meta-data of observation by using concepts of information granules. In the proposed method, uncertainty (diversity) of findings produced by the base classifiers is quantified by interval-based information granules. The discriminative decision model is generated by considering both the bounds and the length of the obtained intervals. We select ten and then fifteen learning algorithms to build a heterogeneous ensemble system and then conducted the experiment on a number of UCI datasets. The experimental results demonstrate that the proposed approach performs better than the benchmark algorithms including six fixed combining methods, one trainable combining method, AdaBoost, Bagging, and Random Subspace.