2017
DOI: 10.1111/itor.12428
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Interactive evolutionary approaches to multiobjective feature selection

Abstract: In feature selection problems, the aim is to select a subset of features to characterize an output of interest. In characterizing an output, we may want to consider multiple objectives such as maximizing classification performance, minimizing number of selected features or cost, etc. We develop a preference‐based approach for multiobjective feature selection problems. Finding all Pareto‐optimal subsets may turn out to be a computationally demanding problem and we still would need to select a solution. Therefor… Show more

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Cited by 6 publications
(3 citation statements)
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“…In recent decades, bioinspired optimization algorithms gained much attention due to their search capability allied to good results in many fields, especially in engineering, in comparison to classic techniques such as gradient‐based models (Holland, 1992; Back et al., 2000; Storn et al., 2005; Castro, 2006; Li et al., 2019). They draw inspiration from natural phenomena, such as the complex behavior of groups of animals or Darwin's theory of evolution (Eberhart et al., 2001; Castro, 2006) to tailor effective search strategies that combine operators for exploring the space of candidate solutions with mechanisms for local refinement (Kazemi et al., 2014; Ozmen et al., 2018).…”
Section: Bioinspired Optimizationmentioning
confidence: 99%
“…In recent decades, bioinspired optimization algorithms gained much attention due to their search capability allied to good results in many fields, especially in engineering, in comparison to classic techniques such as gradient‐based models (Holland, 1992; Back et al., 2000; Storn et al., 2005; Castro, 2006; Li et al., 2019). They draw inspiration from natural phenomena, such as the complex behavior of groups of animals or Darwin's theory of evolution (Eberhart et al., 2001; Castro, 2006) to tailor effective search strategies that combine operators for exploring the space of candidate solutions with mechanisms for local refinement (Kazemi et al., 2014; Ozmen et al., 2018).…”
Section: Bioinspired Optimizationmentioning
confidence: 99%
“…To come up with such results in a suitably short time, the DM must be part of the optimisation process rather than waiting until the process finishes. The main idea is to focus only on the ROI rather than exploring all the design space [9,10], because the benefits from trade-off solutions that lie outside the ROI are very small compared with the computational cost and the efforts required of the DM in analysing unnecessary information [11]. Involving the DM within the optimisation process periodically to provide significant information usually helps with focusing on a sub-part of the design space (see Figure 1).…”
Section: Introductionmentioning
confidence: 99%
“…There are three main factors involved with the profit maximization of the company: waste minimization, surplus minimization, and cost minimization. Therefore, a multiobjective modelling and optimization (Boros, Fehér, Lakner, Niroomand, & Vizvári, 2016;Cardillo, Cascini, Frillici, & Rotini, 2013;Franco, Jablonsky, Leopold-Wildburger, & Montibeller, 2009;Ghasemi & Varaee, 2017;Gholizadeh & Baghchevan, 2017;Gutiérrez, Huerga, & Novo, 2018;Jablonsky, 2014;Jiménez, Bilbao-Terol, & Arenas-Parra, 2018;Kovács & Marian, 2002;Özmen, Karakaya, & Köksalan, 2018;Sanei, Mahmoodirad, Niroomand, Jamalian, & Gelareh, 2017;Taassori et al, 2015) is a suitable approach for incorporating the above-mentioned objectives. Furthermore, when some parameters of the problem have uncertain nature, it must be reflected in the model as well (Fullér, Canós-Darós, & Canós-Darós, 2012;Fullér & Majlender, 2004;Moloudzadeh, Allahviranloo, & Darabi, 2013;Niroomand, Mahmoodirad, Heydari, Kardani, & Hadi-Vencheh, 2017;Salahshour & Allahviranloo, 2013;Salmasnia, Khatami, Baradaran Kazemzadeh, & Zegordi, 2015;Taassori, Niroomand, Uysal, Hadi-Vencheh, & Vizvari, 2016;Wang, Zhou, Li, Zhang, & Chen, 2014).…”
mentioning
confidence: 99%