Kernel-based learning and feature selection analysis for cancer diagnosis

Medjahed, Seyyid Ahmed; Saadi, Tamazouzt Ait; Benyettou, Abdelkader; Ouali, Mohammed

doi:10.1016/j.asoc.2016.12.010

Cited by 98 publications

(44 citation statements)

References 27 publications

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“…The most common stopping condition is the achieved number of iterations (n_ITER), which is sometimes referred to as generations. In the analyzed articles, researchers used the following number of iterations, ordered from the most used ones to the least: 100 [33,[41][42][43], 50 [35,44,45], 500 [46], 200 [47], 70 [34], and 25 [36]. If a composed stopping condition is used, it is always combined with the number of iterations.…”

Section: Defined Stopping Conditionmentioning

confidence: 99%

“…If a composed stopping condition is used, it is always combined with the number of iterations. Some other stopping conditions are: exceeded threshold value [48], optimal solution reached [49,50], and the objective function is equal to 0 [46].…”

Section: Defined Stopping Conditionmentioning

confidence: 99%

“…Many researchers use SVMs as the classifier method to evaluate solutions (e.g., [33,38,[50][51][52]). Researchers in [46] proposed fitness function that is the average of the classification error rate obtained by three classifiers, SVM variations, υ-SVM, C-SVM and LS-SVM in the DA algorithm. The average results were used to prevent generation of a feature subset based on a single classifier.…”

Section: Fitness Functionmentioning

confidence: 99%

“…In [46], researchers proposed a two-sided DA algorithm in which the update of dragonflies' position is done using Equation 6:…”

Section: Update and Move Agentsmentioning

confidence: 99%

“…The used datasets can be divided into three groups (Figure 7), regarding the number of features: Small datasets (up to 150 features), large datasets (more than 2000 features), and mixed datasets between the other two. The first group is composed of many datasets, such as Iris (4 features Some researchers who used only such data are [45,46,50,[64][65][66]. The remaining group is composed of mixed datasets.…”

Section: Datasetsmentioning

confidence: 99%

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Swarm Intelligence Algorithms for Feature Selection: A Review

2018

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The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should be reduced first. Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets. Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems. It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications. With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS. We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories. We propose a unified SI framework and use it to explain different approaches to FS. Different methods, techniques, and their settings are explained, which have been used for various FS aspects. The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed. In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem.

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