Hybrid Swarm Intelligence Algorithms with Ensemble Machine Learning for Medical Diagnosis

Al-Tashi, Qasem; Rais, Helmi Md; Abdulkadir, Said Jadid

doi:10.1109/iccoins.2018.8510615

Cited by 27 publications

(15 citation statements)

References 21 publications

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“…This is a result of filters disregarding the performance of features selected on a classification algorithm [17], [18]. In addition, other researchers defined three types of feature selection which are: filters, wrappers and embedded methods [19], [18]. Embedded integrates the classifier and feature selected into a single process.…”

Section: Related Workmentioning

confidence: 99%

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

et al. 2020

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Feature selection or dimensionally reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multiobjective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multiobjective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost.

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Section: Related Workmentioning

confidence: 99%

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

et al. 2020

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“…Selection of features is an NP-hard issue with large complicated solution spaces [73], [74]. Thus, a robust global search algorithm is needed, and recent multi-objective methods still can enhance further.…”

Section: ) Search Mechanismsmentioning

confidence: 99%

Approaches to Multi-Objective Feature Selection: A Systematic Literature Review

Al-Tashi¹,

et al. 2020

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Feature selection has gained much consideration from scholars working in the domain of machine learning and data mining in recent years. Feature selection is a popular problem in Machine learning with the goal of finding optimal features with increase accuracy. As a result, several studies have been conducted on multi-objective feature selection through numerous multi-objective techniques and algorithms. The objective of this paper is to present a systematic literature review of the challenges and issues of the multi-objective feature selection problem and critically analyses the proposed techniques used to tackle this problem. The conducted review covered all related studies published since 2012 up to 2019. The outcomes of the reviewed of these studies clearly showed that no perfect solution to the multi-objective feature selection problem yet. The authors believed that the conducted review would serve as the main source of the techniques and methods used to resolve the problem of multi-objective feature selection. Furthermore, current challenges and issues are deliberated to find promising research domains for further study.

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“…The respective tests were carried out using KNN. Another study proposed in [38] for the diagnosis of diseases using SVM hybridized a dynamic ant colony system with wavelets transform and singular value decomposition in order to implement a feature selection approach and reduce the high-dimension of the data. The results of both hybrid-based wrappers were seen to be competitive, in terms of accuracy and number of features, with the state of the art of wrappers.…”

Section: Related Work a Wrappers For Feature Selectionmentioning

confidence: 99%

Wrapper for Building Classification Models Using Covering Arrays

et al. 2019

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Wrapper methods are a type of feature selection method that finds a subset of variables to improve the performance of a classifier by removing redundant and irrelevant variables. The use of a wrapper implies that each time a candidate solution is explored, the classifier is evaluated on the quality measures selected (e.g. accuracy or precision). Though robust, this iteration across several candidate solutions can become computationally intensive and time-consuming. In this paper we propose a wrapper, that is based on binary Covering Arrays (CAs), and binary Incremental Covering Arrays (ICAs), that have been widely used for experimental design and fault detection in software and hardware testing. The new wrapper was evaluated with six classifiers on seven data sets. The results show that the CAs and ICAs with strength 6 significantly improve the performance and reduces the number of variables required by the classifier. A comparative analysis of the proposed method against wrappers based on other search approaches such as genetic algorithms (GA) and particle swarm optimization (PSO), shows that the proposed method yields results similar to GA, but not to PSO, with differences to PSO, in accuracy, which in the majority of cases is below 0.04. This lack of accuracy, by which the new wrapper fails to match PSO, is offset by the fact that the user does not need to fine tune algorithm parameters, such as velocity ranges, timing, cognitive coefficient, and social coefficient, while it is also much easier to program in parallel.INDEX TERMS Classification algorithms, covering arrays, random forest, support vector machines, genetic algorithms, particle swarm optimization. HUGO DORADO received the B.Sc. degree in statistics from the Universidad del Valle, Cali, Colombia, in 2013, and the M.Sc. degree in computing from the Universidad del Cauca, Popayán, Colombia, in 2019.He is currently a Research Assistant with the Research Group of Decision and Policy Analysis (DAPA), International Center for Tropical Agriculture (CIAT). His role has been focused in analyzing observational information in agriculture by using analytical methodologies for detecting patterns associated with either high or low yields. His research interests include data mining, computational intelligence, and statistical science.

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Hybrid Swarm Intelligence Algorithms with Ensemble Machine Learning for Medical Diagnosis

Cited by 27 publications

References 21 publications

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

Approaches to Multi-Objective Feature Selection: A Systematic Literature Review

Wrapper for Building Classification Models Using Covering Arrays

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