Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm

This article presents a new adaptive algorithm called FS-SLOA (Feature Selection-Seven Spot Ladybird Optimization Algorithm) which is a meta-heuristic feature selection method based on the foraging behavior of a seven spot ladybird. The new efficient technique has been applied to find the best subset features, which achieves the highest accuracy in classification using three classifiers: the Naive Bayes (NB), the Nearest Neighbors (KNN) and the Support Vector Machine (SVM). The authors' proposed approach has been experimented on four well-known benchmark datasets (Wisconsin Breast cancer, Pima Diabetes, Mammographic Mass, and Dermatology datasets) taken from the UCI machine learning repository. Experimental results prove that the classification accuracy of FS-SLOA is the best performing for different datasets.

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Using Penguins Search Optimization Algorithm for Best Features Selection for Biomedical Data Classification

International Journal of Organizational and Collective Intelligence ◽

10.4018/ijoci.2017100103 ◽

2017 ◽

Vol 7 (4) ◽

pp. 51-62 ◽

Cited By ~ 1

Author(s):

Noria Bidi ◽

Zakaria Elberrichi

Keyword(s):

Feature Selection ◽

Optimization Algorithm ◽

Feature Selection Method ◽

Support Vector ◽

Feature Subset ◽

Biomedical Data ◽

Features Selection ◽

Cancer Data ◽

Search Optimization ◽

Fitness Value

Feature selection is essential to improve the classification effectiveness. This paper presents a new adaptive algorithm called FS-PeSOA (feature selection penguins search optimization algorithm) which is a meta-heuristic feature selection method based on “Penguins Search Optimization Algorithm” (PeSOA), it will be combined with different classifiers to find the best subset features, which achieve the highest accuracy in classification. In order to explore the feature subset candidates, the bio-inspired approach PeSOA generates during the process a trial feature subset and estimates its fitness value by using three classifiers for each case: Naive Bayes (NB), Nearest Neighbors (KNN) and Support Vector Machines (SVMs). Our proposed approach has been experimented on six well known benchmark datasets (Wisconsin Breast Cancer, Pima Diabetes, Mammographic Mass, Dermatology, Colon Tumor and Prostate Cancer data sets). Experimental results prove that the classification accuracy of FS-PeSOA is the highest and very powerful for different datasets.

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A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-190134 ◽

2021 ◽

Vol 24 (4) ◽

pp. 289-301

Author(s):

B. Venkatesh ◽

J. Anuradha

Keyword(s):

Feature Selection ◽

Microarray Data ◽

Classification Accuracy ◽

Performance Metrics ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Svm Classifier ◽

Binary Particle Swarm Optimization ◽

Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

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Feature Selection Method Based on Mutual Information and Support Vector Machine

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800142150021x ◽

2021 ◽

pp. 2150021

Author(s):

Gang Liu ◽

Chunlei Yang ◽

Sen Liu ◽

Chunbao Xiao ◽

Bin Song

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Mutual Information ◽

Classification Accuracy ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Svm Classifier ◽

Standard Data ◽

Feature Dimension

A feature selection method based on mutual information and support vector machine (SVM) is proposed in order to eliminate redundant feature and improve classification accuracy. First, local correlation between features and overall correlation is calculated by mutual information. The correlation reflects the information inclusion relationship between features, so the features are evaluated and redundant features are eliminated with analyzing the correlation. Subsequently, the concept of mean impact value (MIV) is defined and the influence degree of input variables on output variables for SVM network based on MIV is calculated. The importance weights of the features described with MIV are sorted by descending order. Finally, the SVM classifier is used to implement feature selection according to the classification accuracy of feature combination which takes MIV order of feature as a reference. The simulation experiments are carried out with three standard data sets of UCI, and the results show that this method can not only effectively reduce the feature dimension and high classification accuracy, but also ensure good robustness.

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A Novel Support Vector Machine with Class-Dependent Features for Biomedical Data

Pattern Recognition Technologies and Applications ◽

10.4018/978-1-59904-807-9.ch013 ◽

2008 ◽

pp. 284-298 ◽

Cited By ~ 1

Author(s):

Nina Zhou ◽

Lipo Wang

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Biomedical Data

This chapter introduces an approach to class-dependent feature selection and a novel support vector machine (SVM). The relative background and theory are presented for describing the proposed method, and real applications of the method on several biomedical datasets are demonstrated in the end. The authors hope this chapter can provide readers a different view of feature selection method and also the classifier so as to promote more promising methods and applications.

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Classification and Feature Selection Method for Medical Datasets by Brain Storm Optimization Algorithm and Support Vector Machine

Procedia Computer Science ◽

10.1016/j.procs.2019.11.289 ◽

2019 ◽

Vol 162 ◽

pp. 307-315 ◽

Cited By ~ 6

Author(s):

Eva Tuba ◽

Ivana Strumberger ◽

Timea Bezdan ◽

Nebojsa Bacanin ◽

Milan Tuba

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Optimization Algorithm ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Brain Storm Optimization

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Sentiment Analysis Using Hybrid Feature Selection Techniques

UHD Journal of Science and Technology ◽

10.21928/uhdjst.v4n1y2020.pp29-40 ◽

2020 ◽

Vol 4 (1) ◽

pp. 29

Author(s):

Sasan Sarbast Abdulkhaliq ◽

Aso Mohammad Darwesh

Keyword(s):

Machine Learning ◽

Social Media ◽

Feature Selection ◽

Classification Accuracy ◽

Feature Selection Method ◽

Selection Method ◽

Machine Learning Algorithms ◽

Feature Subset Selection ◽

Support Vector ◽

Feature Subset

Nowadays, people from every part of the world use social media and social networks to express their feelings toward different topics and aspects. One of the trendiest social media is Twitter, which is a microblogging website that provides a platform for its users to share their views and feelings about products, services, events, etc., in public. Which makes Twitter one of the most valuable sources for collecting and analyzing data by researchers and developers to reveal people sentiment about different topics and services, such as products of commercial companies, services, well-known people such as politicians and athletes, through classifying those sentiments into positive and negative. Classification of people sentiment could be automated through using machine learning algorithms and could be enhanced through using appropriate feature selection methods. We collected most recent tweets about (Amazon, Trump, Chelsea FC, CR7) using Twitter-Application Programming Interface and assigned sentiment score using lexicon rule-based approach, then proposed a machine learning model to improve classification accuracy through using hybrid feature selection method, namely, filter-based feature selection method Chi-square (Chi-2) plus wrapper-based binary coordinate ascent (Chi-2 + BCA) to select optimal subset of features from term frequency-inverse document frequency (TF-IDF) generated features for classification through support vector machine (SVM), and Bag of words generated features for logistic regression (LR) classifiers using different n-gram ranges. After comparing the hybrid (Chi-2+BCA) method with (Chi-2) selected features, and also with the classifiers without feature subset selection, results show that the hybrid feature selection method increases classification accuracy in all cases. The maximum attained accuracy with LR is 86.55% using (1 + 2 + 3-g) range, with SVM is 85.575% using the unigram range, both in the CR7 dataset.

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A New Feature Selection Method for Sentiment Analysis in Short Text

Journal of Intelligent Systems ◽

10.1515/jisys-2018-0171 ◽

2018 ◽

Vol 29 (1) ◽

pp. 1122-1134

Author(s):

H. M. Keerthi Kumar ◽

B. S. Harish

Keyword(s):

Feature Selection ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Information Gain ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Selection Methods ◽

K Nearest Neighbors ◽

Short Text

Abstract In recent internet era, micro-blogging sites produce enormous amount of short textual information, which appears in the form of opinions or sentiments of users. Sentiment analysis is a challenging task in short text, due to use of formal language, misspellings, and shortened forms of words, which leads to high dimensionality and sparsity. In order to deal with these challenges, this paper proposes a novel, simple, and yet effective feature selection method, to select frequently distributed features related to each class. In this paper, the feature selection method is based on class-wise information, to identify the relevant feature related to each class. We evaluate the proposed feature selection method by comparing with existing feature selection methods like chi-square ( χ2), entropy, information gain, and mutual information. The performances are evaluated using classification accuracy obtained from support vector machine, K nearest neighbors, and random forest classifiers on two publically available datasets viz., Stanford Twitter dataset and Ravikiran Janardhana dataset. In order to demonstrate the effectiveness of the proposed feature selection method, we conducted extensive experimentation by selecting different feature sets. The proposed feature selection method outperforms the existing feature selection methods in terms of classification accuracy on the Stanford Twitter dataset. Similarly, the proposed method performs competently equally in terms of classification accuracy compared to other feature selection methods in most of the feature subsets on Ravikiran Janardhana dataset.

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