Data Centroid Based Multi-Level Fuzzy Min-Max Neural Network
This work addresses classification accuracy issues in neural networks for researchers in machine learning, but it is incremental as it extends an existing method.
The paper tackles the problem of misclassification in overlapped regions of a multi-level fuzzy min-max neural network by introducing a new boundary region and using data centroids to enhance classification accuracy, resulting in consistent improvements on standard datasets.
Recently, a multi-level fuzzy min max neural network (MLF) was proposed, which improves the classification accuracy by handling an overlapped region (area of confusion) with the help of a tree structure. In this brief, an extension of MLF is proposed which defines a new boundary region, where the previously proposed methods mark decisions with less confidence and hence misclassification is more frequent. A methodology to classify patterns more accurately is presented. Our work enhances the testing procedure by means of data centroids. We exhibit an illustrative example, clearly highlighting the advantage of our approach. Results on standard datasets are also presented to evidentially prove a consistent improvement in the classification rate.