Hybrid binary Coral Reefs Optimization algorithm with Simulated Annealing for Feature Selection in high-dimensional biomedical datasets

Yan, CH; Ma, Jingjing; Luo, Huimin; Patel, Ashutosh

doi:10.1016/j.chemolab.2018.11.010

Cited by 112 publications

(45 citation statements)

References 16 publications

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“…A binary GSA, based on Newtonian laws of gravity and motion, was proposed by Rashedi et al [42]. The authors of [43] proposed a hybrid binary CRO with SA for FS in biomedical datasets. A hybrid Harris Hawks Optimizer (HHO) with SA was proposed by the authors of [44].…”

Section: Literature Surveymentioning

confidence: 99%

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

et al. 2020

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Feature selection is a process to reduce the dimension of a dataset by removing redundant features, and to use the optimal subset of features for machine learning or data mining algorithms. This helps to minimize the time requirement to train a learning algorithm as well as to lessen the storage requirement by ignoring the less-informative features. Feature selection can be considered as a combinatorial optimization problem. In this paper, the authors have presented a new feature selection algorithm called Mayfly-Harmony Search (MA-HS) based on two meta-heuristics namely Mayfly Algorithm and Harmony Search. Mayfly Algorithm has not hitherto been used for feature selection problems to the best of the author's knowledge. An S-shaped transfer function is incorporated for converting it into a binary version of Mayfly Algorithm. When different candidate solutions obtained from various regions of the search space using Mayfly Algorithm are taken into the harmony memory and processed by Harmony Search, a superior solution can be ensured. This is the primary reason for proposing a hybrid of Mayfly Algorithm and Harmony Search. Thus, combining harmony search with Mayfly Algorithm leads to an increased exploitation of the search space and an overall improvement in the performance of Mayfly-Harmony Search (MA-HS) algorithm. The proposed algorithm has been applied on 18 UCI datasets and compared with 12 other state-of-the-art meta-heuristic FS methods. Experiments have also been performed on three high-dimensional microarray datasets. The results obtained support the superior performance of the algorithm compared to the other methods. The source code of the proposed algorithm can be found using the link as follows: https://github.com/trin07/MA-HS.

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Section: Literature Surveymentioning

confidence: 99%

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

et al. 2020

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“…In the latest research in 2019, the binary coral reefs optimization algorithm using the SA algorithm is presented by Yan et al 34 to select features in the high-dimensional biological dataset. The proposed method performs better than other methods due to the use of SA on the medical dataset.…”

Section: Related Workmentioning

confidence: 99%

A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

Mohammadzadeh

Gharehchopogh

2020

Computational Intelligence

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Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition-based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar 176

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“…Kozodoi et al [6] levergaed profit measures to propose a wrapper feature selection algorithm using NSGA-II genetic algorithm. Yan et al [40] improved yet another evolutionary algorithm, Coral Reefs Optimization (CRO), to select the best matched feature subsets, called BCROSAT to apply on biomedical data. Arora and Anand [39] introduced a binary variants of a new evolutionary algorithm, the butterfly optimization algorithm (BOA), to select the most important features.…”

Section: Feature Selection For Network Traffic Classificationmentioning

confidence: 99%

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

Mohammadi

Amini

Arabnia

2020

Advances in Intelligent Systems and Computing

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In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach.Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis.

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Hybrid binary Coral Reefs Optimization algorithm with Simulated Annealing for Feature Selection in high-dimensional biomedical datasets

Cited by 112 publications

References 16 publications

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

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