Correction to: An efficient feature subset selection approach for machine learning

In the current epidemic situations, people are facing several mental disorders related to Depression, Anxiety, and Stress (DAS). Numerous scales are developed for computing the levels for DAS, and DAS-21 is one among them. At the same time, machine learning (ML) models are applied widely to resolve the classification problem efficiently, and feature selection (FS) approaches can be designed to improve the classifier results. In this aspect, this paper develops an intelligent feature selection with ML-based risk management (IFSML-RM) for DAS prediction. The IFSML-RM technique follows a two-stage process: quantum elephant herd optimization-based FS (QEHO-FS) and decision tree (DT) based classification. The QEHO algorithm utilizes the input data to select a valuable subset of features at the primary level. Then, the chosen features are fed into the DT classifier to determine the existence or non-existence of DAS. A detailed experimentation process is carried out on the benchmark dataset, and the experimental results showcased the betterment of the IFSML-RM technique in terms of different performance measures.

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Extreme Learning Machine-based Differential Evolution Feature Selection Approach for Feature Subset Selection from Digital Images

International Journal of Applied Research on Information Technology and Computing ◽

10.5958/0975-8089.2016.00013.0 ◽

2016 ◽

Vol 7 (2) ◽

pp. 119

Author(s):

P.T.T. Bharathi ◽

P. Subashini ◽

G.K. Asha

Keyword(s):

Feature Selection ◽

Differential Evolution ◽

Extreme Learning Machine ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Selection Approach ◽

Evolution Feature ◽

Learning Machine ◽

Feature Selection Approach

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Improved Reasoning with Uncertainty Based Significant Feature Subset Selection for Alzheimer’s Disease Detection

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5000.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 3123-3131

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Neurodegenerative Disorder ◽

Early Stage ◽

Subset Selection ◽

Feature Subset Selection ◽

Significant Feature ◽

Feature Subset ◽

Shafer Theory

In this modern era neurodegenerative disorder of undefined causes affects the older adults and it becomes most cause of dementia. The Alzheimer’s disease is one of such neurodegenerative disorder which is very complex and hard to predict in the early stage. With evolving advancement in the field of machine learning, it is possible to predict the early stage of AD and diagnosing in initial stages may produce effect result for their further quality and healthy life. But uncertainty in determination of Alzheimer’s is a toughest challenge for the researchers in the field of machine learning. This paper aims to overcome the uncertainty in discovering dementia and non-dementia victims of Alzheimer’s by devising an improved reasoning with uncertainty based prominent feature subset selection using modified fuzzy dempster shafer theory (IRU-DST). For Alzheimer’s disease prediction the dataset is used form OASIS dataset. The performance of the proposed IRU-DST is validated using fuzzy artificial neural network. The simulation results proved the performance of the IRU-DST achieves better results comparing the other sate of arts, by gaining high accuracy rate and it also minimize the error rate considerably with the ability of handling uncertainty.

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