A Novel Auction-Based Optimization Algorithm and Its Application in Rough Set Feature Selection

The selection of features from data, as one of the most important tasks in data mining, strongly affects the accuracy of classification. The removal of irrelevant and redundant features from data while simultaneously avoiding information loss is the main objective of feature selection. Feature selection is possible using rough set theory and meta-heuristic algorithms. In this paper, a novel auction-based optimization algorithm (ABOA) is proposed to contribute to generating an effective algorithm with a good trade-off between exploration and exploitation. This new algorithm simulates the auction sale process, where bidders offer higher/lower amounts to outbid each other. Auctions are categorized into ascending auctions and descending auctions and thus respectively represent maximization and minimization problems in ABOA. In the first step of the ABOA after initialization, a predefined number of bidders is selected and an auction is performed between them. The winner is selected and another auction is performed between the winner and a predefined number of the winner’s neighbors. The winner of this round of auction is added to the winner list. This process is iterated until a predefined number of winners is found. Finally, one more auction is performed between all the winners on the winner list and the winner of that auction becomes the final winner. The algorithm with different parameter setting scenarios is tested on 25 benchmark test functions. The algorithm with the best results is then used to perform feature selection on 18 UCI datasets. The feature selection and classification accuracy results are compared with state-of-the-art results. The statistical analysis of the results proves the ability of the algorithm to solve optimization problems.