A Novel Hybridized Rough Set and Improved Harmony Search Based Feature Selection for Protein Sequence Classification

Bagyamathi, M.; Inbarani, H. Hannah

doi:10.1007/978-3-319-11056-1_6

Cited by 13 publications

(7 citation statements)

References 61 publications

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“…The feature selection and classification techniques using rough set theory are effectively implemented in many studies [15,16]. An improved HS is embedded with RST for attribute selection to analyze protein sequences [17].…”

Section: Aids (Acquired Immunodeficiency Syndromementioning

confidence: 99%

“…In Section 3 Figure 1 shows the proposed framework that depicts and predicts the anti-HIV-1 peptides with a good accuracy using finest selected features. As the RSIHSQR and RSIHSRR feature selection algorithm has revealed the best result in the previous studies [17], it is also employed for the peptide therapeutic field of this study.…”

Section: Research Motivation and Contributionmentioning

confidence: 99%

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Rough Set Based Classification and Feature Selection Using Improved Harmony Search for Peptide Analysis and Prediction of Anti-HIV-1 Activities

et al. 2022

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AIDS, which is caused by the most widespread HIV-1 virus, attacks the immune system of the human body, and despite the incredible endeavors for finding proficient medication strategies, the continuing spread of AIDS and claiming subsequent infections has not yet been decreased. Consequently, the discovery of innovative medicinal methodologies is highly in demand. Some available therapies, based on peptides, proclaim the treatment for several deadly diseases such as AIDS and cancer. Since many experimental types of research are restricted by the analysis period and expenses, computational methods overcome the issues effectually. In computational technique, the peptide residues with anti-HIV-1 activity are predicted by classification method, and the learning process of the classification is improved with significant features. Rough set-based algorithms are capable of dealing with the gaps and imperfections present in real-time data. In this work, feature selection using Rough Set Improved Harmony Search Quick Reduct and Rough Set Improved Harmony Search Relative Reduct with Rough Set Classification framework is implemented to classify Anti-HIV-1 peptides. The primary objective of the proposed methodology is to predict the peptides with an anti-HIV-1 activity using effective feature selection and classification algorithms incorporated in the proposed framework. The results of the proposed algorithms are comparatively studied with existing rough set feature selection algorithms and benchmark classifiers, and the reliability of the algorithms implemented in the proposed framework is measured by validity measures, such as Precision, Recall, F-measure, Kulczynski Index, and Fowlkes–Mallows Index. The final results show that the proposed framework analyzed and classified the peptides with a high predictive accuracy of 96%. In this study, we have investigated the ability of a rough set-based framework with sequence-based numeric features to classify anti-HIV-1 peptides, and the experimentation results show that the proposed framework discloses the most satisfactory solutions, where it rapidly congregates in the problem space and finds the best reduct, which improves the prediction accuracy of the given dataset.

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Section: Aids (Acquired Immunodeficiency Syndromementioning

confidence: 99%

Section: Research Motivation and Contributionmentioning

confidence: 99%

Rough Set Based Classification and Feature Selection Using Improved Harmony Search for Peptide Analysis and Prediction of Anti-HIV-1 Activities

et al. 2022

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“…where k m = rank sum of m th group n = ∑ is the sample size. The Kruskal-Wallis test is defined through an algorithm I. Algorithm I Input: l X q matrix, where l is the size of attributes and q is the size of instances Output: topmost P attributes To individual attribute f i do i= 1, 2,….l j= 1,2,…….q a rank R j is assigned to individual attribute value r k = ∑ ∈ is calculated for each class k using equation (21) Friedman test for equal sample sizes is a non-parametric method for this type of k dependent class. This alternative hypothesis is tested against the null hypothesis, i.e.…”

Section: Classification Problems 41 Structured Singular Value (Ssv)mentioning

confidence: 99%

“…An important tool proposed by Professor Pawlak in 1982, i.e. Rough Set Theory[21]. It has two parts.…”

mentioning

confidence: 99%

Financial Time-Series Prediction With Feature Selection Using Simplex Method Based Social Spider Optimization Algorithm

Nayak¹,

Das²,

Bhanja³

et al. 2021

INDJCSE

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Financial sector is comprising of rising challenges and complexities. So, to handle time series prediction some recent artificial intelligent techniques are developed. In this paper, a technique named Simplex Method based Social Spider Optimization (SMSSO) is developed to predict time-series datasets like absenteeism at work, energy consumption, blog feedback data, and currency exchange rate. Performance of different artificial intelligence methods are checked with and without some feature selection techniques like ANOVA, Kruskal Walis, and Friedman test. Required features are selected that reduces the size of data to make easy analysis. Recent year data is associated in testing and the previous year's data is used in training. The performance parameters during classification are Mean Square Error (MSE) value and time for execution which is divided into training and testing time. Results show 0.542 as minimum MSE in 1.142 sec of testing time when associated with ANOVA in currency exchange rate, 0.613 MSE in 2.102 sec of testing time when associated with Kruskal Walis in Blog feedback data, and 0.403 MSE in 1.367 sec of testing time when associated with Friedman test using electricity consumption and 0.210 of minimum MSE in 2.102 sec of testing time without any feature selection with blog feedback data in case of SMSSO-NN. The SMSSO-NN results are also compared with different classification algorithms like Rough Set Theory and Structured Singular Value (SSV).

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“…The evaluation was done using support vector machine (SVM) classifiers. We have seen that HSA has provided satisfactory results in the case of FS for holistic Bangla word recognition [36], digit classification [37], email classification [38], epileptic seizure detection [39] and protein sequence classification [40]. However, to best of our knowledge, to date, the HSA-based FS technique has not been applied to FER systems.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm

Saha¹,

Ghosh

et al. 2020

Applied Sciences

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Nowadays, researchers aim to enhance man-to-machine interactions by making advancements in several domains. Facial emotion recognition (FER) is one such domain in which researchers have made significant progresses. Features for FER can be extracted using several popular methods. However, there may be some redundant/irrelevant features in feature sets. In order to remove those redundant/irrelevant features that do not have any significant impact on classification process, we propose a feature selection (FS) technique called the supervised filter harmony search algorithm (SFHSA) based on cosine similarity and minimal-redundancy maximal-relevance (mRMR). Cosine similarity aims to remove similar features from feature vectors, whereas mRMR was used to determine the feasibility of the optimal feature subsets using Pearson’s correlation coefficient (PCC), which favors the features that have lower correlation values with other features—as well as higher correlation values with the facial expression classes. The algorithm was evaluated on two benchmark FER datasets, namely the Radboud faces database (RaFD) and the Japanese female facial expression (JAFFE). Five different state-of-the-art feature descriptors including uniform local binary pattern (uLBP), horizontal–vertical neighborhood local binary pattern (hvnLBP), Gabor filters, histogram of oriented gradients (HOG) and pyramidal HOG (PHOG) were considered for FS. Obtained results signify that our technique effectively optimized the feature vectors and made notable improvements in overall classification accuracy.

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A Novel Hybridized Rough Set and Improved Harmony Search Based Feature Selection for Protein Sequence Classification

Cited by 13 publications

References 61 publications

Rough Set Based Classification and Feature Selection Using Improved Harmony Search for Peptide Analysis and Prediction of Anti-HIV-1 Activities

Rough Set Based Classification and Feature Selection Using Improved Harmony Search for Peptide Analysis and Prediction of Anti-HIV-1 Activities

Financial Time-Series Prediction With Feature Selection Using Simplex Method Based Social Spider Optimization Algorithm

Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm

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