Feature Subset Selection Techniques with Machine Learning

Title

Feature Subset Selection Techniques with Machine Learning

Creator

Chakraborty S.; Islam S.H.; Samanta D.

Description

Scientists and analysts of machine learning and data mining have a problem when it comes to high-dimensional data processing. Variable selection is an excellent method to address this issue. It removes unnecessary and repetitive data, reduces computation time, improves learning accuracy, and makes the learning strategy or data easier to comprehend. This chapterdescribes various commonly used variable selection evaluation metrics before surveying supervised, unsupervised and semi-supervised variable selection techniques that tend to be often employed in machine learningtasks including classification and clustering. Finally, ensuing variable selection difficulties are addressed. Variant selection is an essential topic in machine learning and pattern recognition, and numerous methods have been suggested. This chapter scrutinizesthe performance of various variable selection techniques utilizing public domain datasets. We assessed the quantity of decreased variants and the increase in learning assessment with the selected variable selection techniques and then evaluated and compared each approach based on these measures. The evaluation criteria for the filter model are critical. Meanwhile, the embedded model selects variations during the learning model's training process, and the variable selection result is automatically outputted when the training process is concluded. While the sum of squares of residuals in regression coefficients is less than a constant, Lasso minimizes the sum of squares of residuals, resulting in rigorous regression coefficients. The variables are then trimmed using the AIC and BIC criteria, resulting in a dimension reduction. Lasso-dependent variable selection strategies, such as the Lasso in the regression model and others, provide a high level of stability. Lasso techniques are prone to high computing costs or overfitting difficulties when dealing with high-dimensional data. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Source

EAI/Springer Innovations in Communication and Computing, pp. 159-175.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Coverage

Chakraborty S., JIS University, Dum Dum Cantonment, India; Islam S.H., Indian Institute of Information Technology Kalyani, West Bengal, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 25228595

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-3-030-93088-2_7

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130548827&doi=10.1007%2f978-3-030-93088-2_7&partnerID=40&md5=fe3f57c54310a65762eab863bf2bce3e