Employing Iterative Feature Selection in Fuzzy Rule-Based Binary Classification

TL;DR

This work addresses the gap between feature selection and classification in binary rule-based tasks by proposing an iterative framework that couples fuzzy correlation-based feature reduction with biclustering. It then extracts fuzzy rules via a rule membership function and combines weak rules using AdaBoost to form a strong classifier. The approach is validated on eight UCI datasets, showing improved accuracy, precision, recall, specificity, and AUC relative to peers, with Friedman tests confirming significance. The framework enhances interpretability and performance, and points to future work on more advanced biclustering and neural integrations.

Abstract

The feature selection in a traditional binary classification algorithm is always used in the stage of dataset preprocessing, which makes the obtained features not necessarily the best ones for the classification algorithm, thus affecting the classification performance. For a traditional rule-based binary classification algorithm, classification rules are usually deterministic, which results in the fuzzy information contained in the rules being ignored. To do so, this paper employs iterative feature selection in fuzzy rule-based binary classification. The proposed algorithm combines feature selection based on fuzzy correlation family with rule mining based on biclustering. It first conducts biclustering on the dataset after feature selection. Then it conducts feature selection again for the biclusters according to the feedback of biclusters evaluation. In this way, an iterative feature selection framework is build. During the iteration process, it stops until the obtained bicluster meets the requirements. In addition, the rule membership function is introduced to extract vectorized fuzzy rules from the bicluster and construct weak classifiers. The weak classifiers with good classification performance are selected by Adaptive Boosting and the strong classifier is constructed by "weighted average". Finally, we perform the proposed algorithm on different datasets and compare it with other peers. Experimental results show that it achieves good classification performance and outperforms its peers.

Figures (8)

• Figure 1: Architecture of the proposed algorithm.
• Figure 2: Flow chat of the proposed algorithm.
• Figure 3: Process of extracting fuzzy rules.
• Figure 4: Construction of weak classification based on fuzzy rules.
• Figure 5: Weighted average process based on AdaBoost.

Theorems & Definitions (1)

• Definition 1