Glowworm Swarm Based Informative Attribute Selection Using Support Vector Machines for Simultaneous Feature Selection and Classification

Gurav, Aniket; Nair, Vinay; Gupta, Utkarsh; Valadi, Jayaraman

doi:10.1007/978-3-319-20294-5_3

Cited by 4 publications

(6 citation statements)

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“…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%

“…Fitness function is many times expressed as a classification accuracy. 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.…”

Section: Fitness Functionmentioning

confidence: 99%

“…In [50], the change of glow-worms' positions is done using a step size calculated for each feature. The primary goal of glow-worms is to minimize the distance between features.…”

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%

“…For the same reason as in Table 2, we excluded the Group Hunting section. [49] FA [56] ABC [33,41] 13 GSO [50] FA [53] FA [58,81] ACO [82] ACO [83] Based on the survey, we can state that Bio-Medical Engineering is the most used field for algorithms' testing or applying. Researchers in [58] proposed the Chaos FA algorithm for heart disease prediction; the Bacterial Memetic Algorithm based feature selection for surface EMG-based hand motion recognition in long-term use was suggested by [85].…”

mentioning

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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Section: Defined Stopping Conditionmentioning

confidence: 99%

Section: Fitness Functionmentioning

confidence: 99%

“…In [50], the change of glow-worms' positions is done using a step size calculated for each feature. The primary goal of glow-worms is to minimize the distance between features.…”

Section: Update and Move Agentsmentioning

confidence: 99%