An Independent Rough Set Approach Hybrid with Artificial Bee Colony Algorithm for Dimensionality Reduction

Problem statement: Dimensionality reduction is viewed as an important pre-processing step for pattern recognition and data mining. As the classical rough set model considers the entire attribute set as a whole to find the subset, comparing all possible combinations of sets of attributes is difficult. Approach: In this study, we have introduced an improved Rough Set-based Attribute Reduction (RSAR) namely Independent RSAR hybrid with Artificial Bee Colony (ABC) algorithm, which finds the subset of attributes independently based on decision attributes (classes) at first and then finds the final reduct. Initially the instances are grouped based on decision attributes. Then the Quick Reduct algorithm is applied to find the reduced feature set for each class. To this set of reducts, the ABC algorithm is applied to select a random number of attributes from each set, based on the RSAR model, to find the final subset of attributes. Results: The performance is analyzed with five different medical datasets namely Dermatology, Cleveland Heart, HIV, Lung Cancer and Wisconsin and compared with six other reduct algorithms. The reduct from the proposed approach reaches greater accuracy of 92.36, 86.54, 86.29, 83.03 and 88.70 % respectively. Conclusion: The experiments states that the proposed approach reduces the computational cost and improves the classification accuracy when compared to some classical techniques.