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

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dc.contributor.authorGhosh, Joyoshree
dc.contributor.authorShuvo, Shaon Bhatta
dc.date.accessioned2021-08-11T09:47:34Z
dc.date.available2021-08-11T09:47:34Z
dc.date.issued2019
dc.description.abstractClassification 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.
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/5950
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/5950
dc.language.isoen_US
dc.publisher10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, IEEE
dc.sourceDIU Institutional Repository
dc.subjectDimensionality reduction
dc.subjectDecision trees
dc.subjectClassification algorithms
dc.subjectRandom forests
dc.subjectLinear discriminant analysis
dc.subjectFeature extraction
dc.subjectBreast cancer
dc.titleImproving Classification Model's Performance Using Linear Discriminant Analysis on Linear Data
dc.typeArticle

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