Feature Selection, Clustering, and Prototype Placement for Turbulence Data Sets

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

Download Full-text

Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Sensors ◽

10.3390/s21051816 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1816

Author(s):

Hailun Xie ◽

Li Zhang ◽

Chee Peng Lim ◽

Yonghong Yu ◽

Han Liu

Keyword(s):

Feature Selection ◽

Particle Swarm ◽

Nonlinear Function ◽

Particle Swarm Optimisation ◽

Data Sets ◽

Discriminative Feature ◽

Advanced Search ◽

Selection For ◽

Selection Tasks ◽

New Strategies

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.

Download Full-text

Bayesian Classifier for Sparsity-Promoting Feature Selection

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001415500226 ◽

2015 ◽

Vol 29 (06) ◽

pp. 1550022 ◽

Cited By ~ 1

Author(s):

Danlei Xu ◽

Lan Du ◽

Hongwei Liu ◽

Penghui Wang

Keyword(s):

Feature Selection ◽

Synthetic Data ◽

Original Data ◽

Radar Data ◽

Bayesian Classifier ◽

Classification Model ◽

Data Sets ◽

Data Set ◽

Classification Boundary ◽

Nonlinear Mappings

A Bayesian classifier for sparsity-promoting feature selection is developed in this paper, where a set of nonlinear mappings for the original data is performed as a pre-processing step. The linear classification model with such mappings from the original input space to a nonlinear transformation space can not only construct the nonlinear classification boundary, but also realize the feature selection for the original data. A zero-mean Gaussian prior with Gamma precision and a finite approximation of Beta process prior are used to promote sparsity in the utilization of features and nonlinear mappings in our model, respectively. We derive the Variational Bayesian (VB) inference algorithm for the proposed linear classifier. Experimental results based on the synthetic data set, measured radar data set, high-dimensional gene expression data set, and several benchmark data sets demonstrate the aggressive and robust feature selection capability and comparable classification accuracy of our method comparing with some other existing classifiers.

Download Full-text

Multi Objective Optimization Based Feature Selection Algorithms for Big Data Analytics: A Review

Indian Journal of Artificial Intelligence and Neural Networking ◽

10.54105/ijainn.e1040.121521 ◽

2021 ◽

pp. 1-4

Author(s):

Aakriti Shukla ◽

◽

Dr Damodar Prasad Tiwari ◽

Keyword(s):

Feature Selection ◽

Big Data ◽

Data Science ◽

Big Data Analytics ◽

Data Sets ◽

Big Data Applications ◽

High Workload ◽

Long Time ◽

Analytical Result ◽

Result Analysis

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

Download Full-text

A novel feature selection algorithm based on damping oscillation theory

PLoS ONE ◽

10.1371/journal.pone.0255307 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255307

Author(s):

Fujun Wang ◽

Xing Wang

Keyword(s):

Feature Selection ◽

Optimization Algorithm ◽

Euclidean Distance ◽

Oscillation Theory ◽

Feature Subset Selection ◽

Support Vector ◽

Data Sets ◽

Feature Subset ◽

Selection Algorithm ◽

Filter Model

Feature selection is an important task in big data analysis and information retrieval processing. It reduces the number of features by removing noise, extraneous data. In this paper, one feature subset selection algorithm based on damping oscillation theory and support vector machine classifier is proposed. This algorithm is called the Maximum Kendall coefficient Maximum Euclidean Distance Improved Gray Wolf Optimization algorithm (MKMDIGWO). In MKMDIGWO, first, a filter model based on Kendall coefficient and Euclidean distance is proposed, which is used to measure the correlation and redundancy of the candidate feature subset. Second, the wrapper model is an improved grey wolf optimization algorithm, in which its position update formula has been improved in order to achieve optimal results. Third, the filter model and the wrapper model are dynamically adjusted by the damping oscillation theory to achieve the effect of finding an optimal feature subset. Therefore, MKMDIGWO achieves both the efficiency of the filter model and the high precision of the wrapper model. Experimental results on five UCI public data sets and two microarray data sets have demonstrated the higher classification accuracy of the MKMDIGWO algorithm than that of other four state-of-the-art algorithms. The maximum ACC value of the MKMDIGWO algorithm is at least 0.5% higher than other algorithms on 10 data sets.

Download Full-text

Q-Learning with Fisher Score for Feature Selection of Large-Scale Data Sets

Knowledge Science, Engineering and Management - Lecture Notes in Computer Science ◽

10.1007/978-3-030-82147-0_25 ◽

2021 ◽

pp. 306-318

Author(s):

Min Gan ◽

Li Zhang

Keyword(s):

Feature Selection ◽

Large Scale ◽

Data Sets ◽

Fisher Score ◽

Q Learning ◽

Large Scale Data ◽

Scale Data ◽

Large Scale Data Sets ◽

Selection Of

Download Full-text

Feature Selection for High-Dimensional and Imbalanced Biomedical Data Based on Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm

Genes ◽

10.3390/genes11070717 ◽

2020 ◽

Vol 11 (7) ◽

pp. 717

Author(s):

Garba Abdulrauf Sharifai ◽

Zurinahni Zainol

Keyword(s):

Feature Selection ◽

Optimization Algorithm ◽

Imbalanced Data ◽

High Dimensional ◽

Data Sets ◽

Biomedical Data ◽

Data Set ◽

Grasshopper Optimization Algorithm ◽

Imbalanced Class ◽

Grasshopper Optimization

The training machine learning algorithm from an imbalanced data set is an inherently challenging task. It becomes more demanding with limited samples but with a massive number of features (high dimensionality). The high dimensional and imbalanced data set has posed severe challenges in many real-world applications, such as biomedical data sets. Numerous researchers investigated either imbalanced class or high dimensional data sets and came up with various methods. Nonetheless, few approaches reported in the literature have addressed the intersection of the high dimensional and imbalanced class problem due to their complicated interactions. Lately, feature selection has become a well-known technique that has been used to overcome this problem by selecting discriminative features that represent minority and majority class. This paper proposes a new method called Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm (rCBR-BGOA); rCBR-BGOA has employed an ensemble of multi-filters coupled with the Correlation-Based Redundancy method to select optimal feature subsets. A binary Grasshopper optimisation algorithm (BGOA) is used to construct the feature selection process as an optimisation problem to select the best (near-optimal) combination of features from the majority and minority class. The obtained results, supported by the proper statistical analysis, indicate that rCBR-BGOA can improve the classification performance for high dimensional and imbalanced datasets in terms of G-mean and the Area Under the Curve (AUC) performance metrics.

Download Full-text

Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data

BMC Genomics ◽

10.1186/s12864-020-07038-3 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Da Xu ◽

Jialin Zhang ◽

Hanxiao Xu ◽

Yusen Zhang ◽

Wei Chen ◽

...

Keyword(s):

Gene Expression ◽

Feature Selection ◽

Therapeutic Targets ◽

Genomic Data ◽

Data Sets ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Model Learning ◽

Data Set ◽

Multi Scale

Abstract Background The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods. To enhance interpretability and overcome this problem, we developed a novel feature selection algorithm. In the meantime, complex genomic data brought great challenges for the identification of biomarkers and therapeutic targets. The current some feature selection methods have the problem of low sensitivity and specificity in this field. Results In this article, we designed a multi-scale clustering-based feature selection algorithm named MCBFS which simultaneously performs feature selection and model learning for genomic data analysis. The experimental results demonstrated that MCBFS is robust and effective by comparing it with seven benchmark and six state-of-the-art supervised methods on eight data sets. The visualization results and the statistical test showed that MCBFS can capture the informative genes and improve the interpretability and visualization of tumor gene expression and single-cell sequencing data. Additionally, we developed a general framework named McbfsNW using gene expression data and protein interaction data to identify robust biomarkers and therapeutic targets for diagnosis and therapy of diseases. The framework incorporates the MCBFS algorithm, network recognition ensemble algorithm and feature selection wrapper. McbfsNW has been applied to the lung adenocarcinoma (LUAD) data sets. The preliminary results demonstrated that higher prediction results can be attained by identified biomarkers on the independent LUAD data set, and we also structured a drug-target network which may be good for LUAD therapy. Conclusions The proposed novel feature selection method is robust and effective for gene selection, classification, and visualization. The framework McbfsNW is practical and helpful for the identification of biomarkers and targets on genomic data. It is believed that the same methods and principles are extensible and applicable to other different kinds of data sets.

Download Full-text