A novel approach of unsupervised feature selection using iterative shrinking and expansion algorithm

Gowda, V. Dankan; Sharma, Avinash; Sarma, Parismita; Hussain, Naziya; Dixit, Santosh Kumar; Gupta, Anand Kumar

doi:10.47974/JIM-1678

Open Access Research Article

A novel approach of unsupervised feature selection using iterative shrinking and expansion algorithm

^*V. Dankan GowdaCorresponding authordankan.v@bmsit.inDepartment of Electronics and Communication EngineeringBMS Institute of Technology and ManagementBangalore, Karnataka, IndiaView full profile → , Avinash Sharmaasharma@mmumullana.orgDepartment of Computer Science and EngineeringGlobal Academy of TechnologyBengaluru, Karnataka, IndiaView full profile → , Parismita Sarmaparismita.sarma@gmail.comDepartment of Information TechnologyGauhati UniversityGuwahati, Assam, IndiaView full profile → , Naziya Hussainnaziyahussain@gmail.comSchool of ComputersIPS AcademyIndore, Madhya Pradesh, IndiaView full profile → , Santosh Kumar Dixitskdixit@ptn.amity.eduAmity School of Engineering and Technology (ASET)Amity UniversityPatna, Bihar, IndiaView full profile → , Anand Kumar GuptaGanand40@yahoo.co.inDepartment of Information TechnologyBlueCrest UniversityMonrovia, LiberiaView full profile →

* Corresponding author · click or hover a name for details

pp. 519–530Vol. 26Issue 3April 2023DOI: 10.47974/JIM-1678XML

Published Online:: 01 Apr 2023
Article type:: Research Article
Language:: EN
Article no.:: JIM-1678
Pages:: 519–530

Abstract

An major constraint in the realm of feature selection is that users choose the ideal number of characteristics that must be picked. In this article, an effort is made to automate the process of determining a suitable value for the appropriate the quantity of characteristics that must be chosen for better recognition tasks. To estimate the ideal amount of features that should be maintained for properly describing the data, we use the dense subgraph discovery approach for this goal. Notably, the existing methods uses a similar kind of approach called the dense subgraph finding. But the earlier approach obtains a single dense subgraph, while the task of dense subgraphs finding obtains a number of dense subgraphs that are important for learning the internal structure of any network. Thus, dense subgraphs finding is likely to be adopted as a natural choice for realizing the complex relations among the nodes of massive real-life networks, such as biological networks, social networks.

Keywords

Shrinking algorithm Expansion algorithm Subgragh Pattern recognition Emotion recognition and machine learning

Subject Classifications

Primary 93A30Secondary 49K1565Y1065F08 and 93B47

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