A survey of nature-inspired algorithms for feature selection to identify Parkinson's disease

Shrivastava, Prashant; Shukla, Anupam; Vepakomma, Praneeth; Bhansali, Neera; Verma, Kshitij

doi:10.1016/j.cmpb.2016.07.029

Cited by 73 publications

(32 citation statements)

References 16 publications

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“…al. 2017 [36]. The present accuracy of 90.32% from one parameter and 100% from all the left leg gait parameters taken together has been at par to, even…”

Section: Accepted Manuscriptmentioning

confidence: 40%

An automatic non-invasive method for Parkinson's disease classification

Joshi

Khajuria

Joshi

2017

Computer Methods and Programs in Biomedicine

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“…al. 2017 [36]. The present accuracy of 90.32% from one parameter and 100% from all the left leg gait parameters taken together has been at par to, even…”

Section: Accepted Manuscriptmentioning

confidence: 40%

An automatic non-invasive method for Parkinson's disease classification

Joshi

Khajuria

Joshi

2017

Computer Methods and Programs in Biomedicine

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“…To overcome sub-optimal convergence, the concept of meta-heuristics search algorithms are applied to search for optimal features (genes) that can best represent the entire original features from microarray datasets (Shrivastava et al 2017). Most of these search systems are nature inspired such as genetic algorithms, genetic programming, simulated annealing, and particle swarm optimisation are exploited which presents different level of fitness.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Flower Pollination Algorithm and Support Vector Machine for Breast Cancer Classification

Dankolo

Radzi²,

Salehuddin³

et al. 2018

JTMB

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Microarray technology is a system that enable experts to examine gene profile at molecular level for early disease detection. Machine learning algorithms such as classification are used in detection of dieses from data generated by microarray. It increases the potentials of classification and diagnosis of many diseases such as cancer at gene expression level. Though, numerous difficulties may affect the performance of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data preprocessing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper proposed a new technique for feature selection and classification of breast cancer based on Flower Pollination algorithm (FPA) and Support Vector machine (SVM) using microarray data. The result for this research reveals that FPA-SVM is promising by outperforming the state of the earth Particle Swam Optimization algorithm with 80.11% accuracy.

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“…After the process, selected features might be possibility half of the size of the original feature dataset. Bat Algorithm (BA) is used to find the optimal features in [14,15]. Since, BA is a powerful technique to optimize the number of features, this algorithm is combined with another technique such as rough set [16,17].…”

Section: Introductionmentioning

confidence: 99%

Bat algorithm and k-means techniques for classification performance improvement

Mohamed

Yusof

Wahid

et al. 2019

IJEECS

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This paper presents Bat Algorithm and K-Means techniques for classification performance improvement. The objective of this study is to investigate efficiency of Bat Algorithm in discrete dataset and to find the optimum feature in discrete dataset. In this study, one technique that comprise the discretization technique and feature selection technique have been proposed. Our contribution is in two process of classification: pre-processing and feature selection process. First, to proposed discretization techniques called as BkMD, where we hybrid Bat Algorithm technique and K-Means classifier. Second, to proposed BkMDFS as feature selection technique where Bat Algorithm is embed into BkMD. In order to evaluate our proposed techniques, 14 continuous dataset from various applications are used in experiment. From the experiment, results show that BkMDFS outperforms in most performance measures. Hence it shows that, Bat Algorithm have potential to be one of the discretization technique and feature selection technique.

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A survey of nature-inspired algorithms for feature selection to identify Parkinson's disease

Cited by 73 publications

References 16 publications

An automatic non-invasive method for Parkinson's disease classification

An automatic non-invasive method for Parkinson's disease classification

Hybrid Flower Pollination Algorithm and Support Vector Machine for Breast Cancer Classification

Bat algorithm and k-means techniques for classification performance improvement

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