What question did this study set out to answer?

This research aims to evaluate how Linear Discriminant Analysis (LDA) enhances the performance of classification models.

July 1, 2019

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

Key Points

This research aims to evaluate how Linear Discriminant Analysis (LDA) enhances the performance of classification models.
Applied Linear Discriminant Analysis to various datasets for dimensionality reduction
Utilized multiple classification algorithms for performance comparison
Analyzed improvements in model accuracy and efficiency after applying LDA
Classification models showed enhanced accuracy by up to 20% with LDA implementation
Dimensionality reduction helped in decreasing computational time by approximately 30%
Overall performance metrics, such as precision and recall, improved significantly with LDA

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 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.

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Cite This Study

Ghosh et al. (Mon,) studied this question.

synapsesocial.com/papers/6a1ecfab94615786b59a8993 https://doi.org/https://doi.org/10.1109/icccnt45670.2019.8944632

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