Employing Iterative Feature Selection in Fuzzy Rule-Based Binary
  Classification
Haoning Li Cong Wang Qinghua Huang
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.