Evolutionary computing for clinical dataset classification using a novel feature selection algorithm

Sheth, P. D.; Patil, S. T.; Dhore, M. L.

doi:10.1016/j.jksuci.2020.12.012

Cited by 12 publications

(8 citation statements)

References 17 publications

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“…Kiziloz et al (2018) considered feature selection as a multiobjective optimization problem and proposed novel variants of multiobjective Teaching‐Learning‐Based Optimization for the feature selection task. Sheth et al (2020) proposed a multiobjective Jaya Optimization Algorithm for obtaining the optimal subset of features. The proposed model was applied to many clinical datasets.…”

Section: Related Workmentioning

confidence: 99%

Predicting the severity of COVID‐19 patients using a multi‐threaded evolutionary feature selection algorithm

et al. 2022

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The COVID-19 pandemic has huge effects on the global community and an extreme burden on health systems. There are more than 185 million confirmed cases and 4 million deaths as of July 2021. Besides, the exponential rise in COVID-19 cases requires a quick prediction of the patients' severity for better treatment. In this study, we propose a Multi-threaded Genetic feature selection algorithm combined with Extreme Learning Machines (MG-ELM) to predict the severity level of the COVID-19 patients. We conduct a set of experiments on a recently published real-world dataset.We reprocess the dataset via feature construction to improve the learning performance of the algorithm. Upon comprehensive experiments, we report the most impactful features and symptoms for predicting the patients' severity level. Moreover, we investigate the effects of multi-threaded implementation with statistical analysis. In order to verify the efficiency of MG-ELM, we compare our results with traditional and state-of-the-art techniques. The proposed algorithm outperforms other algorithms in terms of prediction accuracy.

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Section: Related Workmentioning

confidence: 99%

Predicting the severity of COVID‐19 patients using a multi‐threaded evolutionary feature selection algorithm

et al. 2022

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“…Single objective wrapper techniques generally serve the purpose by restricting the length of the feature set, or by enhancing the classification efficiency, or by aggregating these targets [41]. For a better understanding of the single-objective evolutionary methods for solving FS task the interested reader can refer to DA [21], SSA [26], [42], HS [43], TLBO [28], grasshopper optimization [44], Jaya algorithm [22], [45], HHO [18], atom search [46], SMO [24], SHO [25], CS [47], ALO [48], ABC [30], FOA [49], FPA [50], and WOA [51], [52] etc.…”

Section: Related Workmentioning

confidence: 99%

A Binary Multi-Objective Chimp Optimizer With Dual Archive for Feature Selection in the Healthcare Domain

et al. 2022

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Medical datasets frequently include vast feature sets with numerous features that are related to one another. As a result, the curse of dimensionality affects learning from a medical dataset to discover significant characteristics, making it necessary to minimize the feature set. Feature selection (FS) is a major step in classification and also in reducing the dimension. This study attempts a novel Binary Multi-objective Chimp Optimization Algorithm (BMOChOA) with dual archive and k-nearest neighbors (KNN) classifier for mining relevant aspects from medical data. In this research, 12 versions of BMOChOA are implemented based on the group information and types of chaotic functions used. The best Pareto front obtained from suggested BMOChOA variations is compared with three benchmark multi-objective FS methods by taking 14 popular medical datasets of variable dimensions. By analyzing the experimental outputs using four multi-objective performance evaluators, it is found that the proposed FS method is superior in finding the best trade-off between the two objective functions: the number of features and classification performance.

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“…Figure 2 shows the possible numbers of selected features that have around half the number of available features with greater chance being selected than others. See algorithm 3 (Line [15][16][17][18][19][20][21][22].…”

Section: B New Initialization Mechanismmentioning

confidence: 99%

“…FS is an optimization problem [16]. One of the most efficient popular methods used to generate candidate solutions for optimization problems is Nature Inspired Algorithms (NIAs) algorithms [17]- [19]. NIAs, developed using characteristics of biological systems, are a population meta-heuristic class of algorithms that improves concurrency of computing multiple candidate solutions [20]- [22].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Binary Cuckoo Search With Frequent Values and Rough Set Theory for Feature Selection

Alia¹,

Taweel

2021

IEEE Access

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Redundant and irrelevant features in datasets decrease classification accuracy, and increase computational time of classification algorithms, overfitting problem and complexity of the underlying classification model. Feature selection is a preprocessing technique used in classification algorithms to improve the selection of relevant features. Several approaches that combine Rough Set Theory (RST) with Nature Inspired Algorithms (NIAs) have been used successfully for feature selection. However, due to the inherit limitations of RST for some data types and the inefficient convergence of NIAs for high dimensional datasets, these approaches have mainly focused on a specific type of low dimensional nominal dataset. This paper proposes a new filter feature selection approach based on Binary Cuckoo Search (BCS) and RST, which is more efficient for low and high dimensional nominal, mixed and numerical datasets. It enhances BCS by developing a new initialization and global update mechanisms to increase the efficiency of convergence for high dimensional datasets. It also develops a more efficient objective function for numerical, mixed and nominal datasets. The proposed approach was validated on 16 benchmark datasets; 4 nominal, 4 mixed and 8 numerical drawn from the UCI repository. It was also evaluated against standard BCS; five NIAs with fuzzy RST approaches; two popular traditional FS approaches; and multi objective evolutionary, Genetic, and Particle Swarm Optimization (PSO) algorithms. Decision tree and naive Bayes algorithms were used to measure the classification performance of the proposed approach. The results show that the proposed approach achieved improved classification accuracy while minimizing the number of features compared to other state-of-the-art methods. The code is available at https://github.com/abualia4/EBCS.

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Evolutionary computing for clinical dataset classification using a novel feature selection algorithm

Cited by 12 publications

References 17 publications

Predicting the severity of COVID‐19 patients using a multi‐threaded evolutionary feature selection algorithm

Predicting the severity of COVID‐19 patients using a multi‐threaded evolutionary feature selection algorithm

A Binary Multi-Objective Chimp Optimizer With Dual Archive for Feature Selection in the Healthcare Domain

Enhanced Binary Cuckoo Search With Frequent Values and Rough Set Theory for Feature Selection

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