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

被引：0

作者：

Ghosh, Joyoshree ^{[1
]}

Shuvo, Shaon Bhatta ^{[1
]}

机构：

[1] Daffodil Int Univ, Dept Comp Sci & Engn, Dhaka, Bangladesh

来源：

2019 10TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT) | 2019年

关键词：

Classification; Linear Discriminant Analysis; Dimensionality Reduction; Feature Extraction;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 which 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.

引用

页数：5