Modified Dominance-based soft set Approach for Feature Selection

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Soft Set ◽  
Feature Reduction ◽  
Research Model ◽  
Selection Algorithm ◽  
Big Data Applications ◽  
Segmented Nucleus ◽  
Experimental Findings

Big data analysis applications in the field of medical image processing have recently increased rapidly. Feature reduction plays a significant role in eliminating irrelevant features and creating a successful research model for Big Data applications. Fuzzy clustering is used for the segment of the nucleus. Various features, including shape, texture, and color-based features, have been used to address the segmented nucleus. The Modified Dominance Soft Set Feature Selection Algorithm (MDSSA) is intended in this paper to determine the most important features for the classification of leukaemia images. The results of the MDSSA are evaluated using the variance analysis called ANOVA. In the dataset extracted function, the MDSSA selected 17 percent of the features that were more promising than the existing reduction algorithms. The proposed approach also reduces the time needed for further analysis of Big Data. The experimental findings confirm that the performance of the proposed reduction approach is higher than other approaches.

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

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.

Efficient Classification of DDoS Attacks Using an Ensemble Feature Selection Algorithm

2017 ◽  
Vol 29 (1) ◽  
pp. 71-83 ◽  
Author(s):  
Khundrakpam Johnson Singh ◽  
Tanmay De
Keyword(s):  
Feature Selection ◽  
Denial Of Service ◽  
Computation Time ◽  
Threshold Value ◽  
Ddos Attacks ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Cyber Threats ◽  
The Given

Abstract In the current cyber world, one of the most severe cyber threats are distributed denial of service (DDoS) attacks, which make websites and other online resources unavailable to legitimate clients. It is different from other cyber threats that breach security parameters; however, DDoS is a short-term attack that brings down the server temporarily. Appropriate selection of features plays a crucial role for effective detection of DDoS attacks. Too many irrelevant features not only produce unrelated class categories but also increase computation overhead. In this article, we propose an ensemble feature selection algorithm to determine which attribute in the given training datasets is efficient in categorizing the classes. The result of the ensemble algorithm when compared to a threshold value will enable us to decide the features. The selected features are deployed as training inputs for various classifiers to select a classifier that yields maximum accuracy. We use a multilayer perceptron classifier as the final classifier, as it provides better accuracy when compared to other conventional classification models. The proposed method classifies the new datasets into either attack or normal classes with an efficiency of 98.3% and also reduces the overall computation time. We use the CAIDA 2007 dataset to evaluate the performance of the proposed method using MATLAB and Weka 3.6 simulators.

scholarly journals Relevant and Non-Redundant Feature Selection for Cancer Classification and Subtype Detection

Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4297
Author(s):  
Pratip Rana ◽  
Phuc Thai ◽  
Thang Dinh ◽  
Preetam Ghosh
Keyword(s):  
Feature Selection ◽  
Gene Selection ◽  
False Negative ◽  
Multiclass Classification ◽  
Feature Reduction ◽  
Control Group ◽  
Normal Sample ◽  
Classification Problems ◽  
Column Subset Selection

Biologists seek to identify a small number of significant features that are important, non-redundant, and relevant from diverse omics data. For example, statistical methods such as LIMMA and DEseq distinguish differentially expressed genes between a case and control group from the transcript profile. Researchers also apply various column subset selection algorithms on genomics datasets for a similar purpose. Unfortunately, genes selected by such statistical or machine learning methods are often highly co-regulated, making their performance inconsistent. Here, we introduce a novel feature selection algorithm that selects highly disease-related and non-redundant features from a diverse set of omics datasets. We successfully applied this algorithm to three different biological problems: (a) disease-to-normal sample classification; (b) multiclass classification of different disease samples; and (c) disease subtypes detection. Considering the classification of ROC-AUC, false-positive, and false-negative rates, our algorithm outperformed other gene selection and differential expression (DE) methods for all six types of cancer datasets from TCGA considered here for binary and multiclass classification problems. Moreover, genes picked by our algorithm improved the disease subtyping accuracy for four different cancer types over state-of-the-art methods. Hence, we posit that our proposed feature reduction method can support the community to solve various problems, including the selection of disease-specific biomarkers, precision medicine design, and disease sub-type detection.

scholarly journals A new theoretical understanding of big data analytics capabilities in organizations: a thematic analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Renu Sabharwal ◽  
Shah Jahan Miah
Keyword(s):  
Big Data ◽  
Thematic Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Development Theory ◽  
Research Model ◽  
Theoretical Understanding ◽  
The Past ◽  
Validity Assessment

AbstractBig Data Analytics (BDA) usage in the industry has been increased markedly in recent years. As a data-driven tool to facilitate informed decision-making, the need for BDA capability in organizations is recognized, but few studies have communicated an understanding of BDA capabilities in a way that can enhance our theoretical knowledge of using BDA in the organizational domain. Big Data has been defined in various ways and, the past literature about the classification of BDA and its capabilities is explored in this research. We conducted a literature review using PRISMA methodology and integrated a thematic analysis using NVIVO12. By adopting five steps of the PRISMA framework—70 sample articles, we generate five themes, which are informed through organization development theory, and develop a novel empirical research model, which we submit for validity assessment. Our findings improve effectiveness and enhance the usage of BDA applications in various Organizations.

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