Gene selection of non-small cell lung cancer data for adjuvant chemotherapy decision using cell separation algorithm

Jaddi, Najmeh Sadat; Abadeh, Mohammad Saniee

doi:10.1007/s10489-020-01740-1

Cited by 3 publications

(3 citation statements)

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“…A trader algorithm was proposed in [60]. The cell separation algorithm (CSA) [18] imitates cell separation action by using a differential centrifugation process involving multiple centrifugation steps and increasing the rotor speed in each step.…”

Section: Related Workmentioning

confidence: 99%

“…Nature-inspired meta-heuristic algorithms, which are based on the behavior of physical or biological systems, have become particularly successful in many fields and optimization problems over the past years. Some applications of optimization problems include: timetabling and scheduling [1][2][3][4], industry [5][6][7], data mining [8][9][10][11][12][13][14][15][16][17][18][19], engineering [20][21][22], pattern recognition [23][24][25][26], and economics [27,28]. In the field of data mining, classification is one of the key tasks and feature selection is an important pre-processing step for successful classification.…”

Section: Introductionmentioning

confidence: 99%

“…1 illustrates a schematic analysis of the exploration versus exploitation in some traditional algorithms. This figure shows the reason for interests and attempts to introduce perfect alternative algorithms for solving complex problems in recent researches [18,19,[38][39][40][41]. In this paper, a new optimization algorithm that can be applied for rough set feature selection is proposed.…”

Section: Introductionmentioning

confidence: 99%

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

Jaddi

Abdullah

2021

IEEE Access

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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.

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Section: Related Workmentioning

confidence: 99%