Improving Classification Model's Performance Using Linear Discriminant Analysis on Linear Data

dc.contributor.author	Ghosh, Joyoshree
dc.contributor.author	Shuvo, Shaon Bhatta
dc.date.accessioned	2021-08-11T09:47:34Z
dc.date.available	2021-08-11T09:47:34Z
dc.date.issued	2019
dc.identifier.uri	http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/5950
dc.description.abstract	Classification is a supervised learning technique for predicting the class of given data points. Before doing classification, it is essential to build a classification model using classification algorithms. There are several classification algorithms that can be used for prediction. Linear Discriminant Analysis (LDA) is used for reducing the dimensionality of datasets. This paper represents how LDA improves different classification model's performance.	en_US
dc.language.iso	en_US	en_US
dc.publisher	10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, IEEE	en_US
dc.subject	Dimensionality reduction	en_US
dc.subject	Decision trees	en_US
dc.subject	Classification algorithms	en_US
dc.subject	Random forests	en_US
dc.subject	Linear discriminant analysis	en_US
dc.subject	Feature extraction	en_US
dc.subject	Breast cancer	en_US
dc.title	Improving Classification Model's Performance Using Linear Discriminant Analysis on Linear Data	en_US
dc.type	Article	en_US