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

Abstract

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 auctionbased 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.

Multisensor Dynamic Alliance Control Problem Based on Fuzzy Set Theory in the Mission of Target Detecting and Tracking

“…The data used to support the findings of this study have been deposited in the references [13,[17][18][19][20][21][22][23] in the paper.…”

“…Algorithms. To verify the effectiveness of the proposed algorithm, we compared the centralized intelligent algorithm (particle swarm optimization algorithm (PSO) [17], bee colony optimization algorithm (BCO) [18]) and distributed intelligent algorithm (auction algorithm (AA) [19]) as the comparison algorithm.…”

“…Multisensor Alliance Scheme under Different Algorithms. To verify the effectiveness of the algorithm proposed in this paper, we compare the centralized intelligent algorithm (PSO [17], BCO [18]) and distributed algorithm (AA [19]) as the comparison algorithm. Further, it analyzes the effectiveness of the alliance scheme based on target tracking tasks obtained by using the fuzzy set theory in this paper.…”

“…Swarm intelligence algorithm is primarily divided into centralized algorithms, such as Harris hawk algorithm (HHO) [14], whale algorithm (WOA) [15], slime mold algorithm (SMA) [16], bee colony algorithm (BCO) [17], and particle swarm optimization algorithm (PSO) [18]. In addition, auction algorithm (AA) [19] and contract network algorithm (CNA) [20] are distributed algorithms. The centralized algorithm transmits all data to the fusion center to calculate the optimal solution with more time and energy.…”

ABOA-CNN: auction-based optimization algorithm with convolutional neural network for pulmonary disease prediction

“…The features related to lung infections such as fibrosis, pneumonia, cardiomegaly, and normal from the X-ray images are selected with the adoption of the ABOA algorithm [22]. This relies on the auction behavior, and here, each feature acted the role of the bidder.…”