Feature Dimension Reduction Using Euclidean Distance Oriented Similarity Based Rough Set Model - Mondal, AC; Kolay, S (International Journal of Computer Sciences and Engineering, 2025)
In machine learning, a very high dimensional data reduces the performance of a classifier. To overcome this, a relevant feature dimension reduction algorithm can be applied before applying any classification algorithm. Rough set theory [1] is a very good tool to reduce the feature dimension of an information system or decision system. However, if a decision system contains real-valued data, we cannot apply directly the rough set theory. Various extensions to rough set can be used to handle this kind of data. Among them, Fuzzy-Rough set theory [2], similarity based rough set model [3] are interesting. We propose an algorithm for dimension reduction using Euclidean distance oriented similarity based rough set model. To show the effectiveness of the algorithm, we take Grammatical Facial Expression Dataset from UCI Machine Learning Repository, created by Freitas et al. [4] and applied KNN classifier before and after feature dimension reduction.