Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Nagaraj, Akash; J., Jayapriya; S., Deepa; M., Vinay Title A name given to the resource Comparative Analysis of Classification Models Using Various Feature Sets Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Electrical Engineering;Volume;1269;pp.141-154 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-9515-4_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-9515-4_11</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003894987?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003894987?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nagaraj A., CHRIST (Deemed to be University), Bangalore, India; J. J., CHRIST (Deemed to be University), Bangalore, India; S. D., CHRIST (Deemed to be University), Bangalore, India; M. V., CHRIST (Deemed to be University), Bangalore, India Description An account of the resource Feature selection is a fundamental step in Machine Learning (ML) that involves choosing some input data that would enhance the model performance. The model is able to run faster using lesser computational resources while giving reasonable results. Hence, feature selection as important as selection of a good model. In this chapter the aim is to analyze how the performance of different multiclass classification algorithms is affected on different features. The algorithms used are K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Convolutional Neural Network (CNN) on the CIFAR-10 dataset. To obtain the new dataset with modified features, we use dimension reduction methods on the original dataset. The new dataset is at least 500x smaller, and we have noticed that in the best case scenarios reducing dimensions reduces the accuracy score only marginally. The SVM is the most consistent among the experimented models. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource CNN; KNN; LDA; NMDS; PCA; SVM; tSNE Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18761100; ISBN: 978-981979514-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online