Please use this identifier to cite or link to this item:
https://elibrary.tucl.edu.np/handle/123456789/10660
Title: | A Comparative Study of Classification Algorithms for Cancer Datasets |
Authors: | Kandel, Ishwari Prasad |
Keywords: | Micro-array;Gene expression data;Breast cancer;Voted perceptron |
Issue Date: | 2015 |
Publisher: | Department of Computer Science & Information Technology |
Institute Name: | Central Department of Computer Science and Information Technology |
Level: | Masters |
Abstract: | Classification algorithms of data mining have been successfully applied in the recent years to predict cancer based on Micro-array Gene Expression Data. Various classification algorithms can be applied on such Micro-array Gene Expression Data to devise methods that can predict the occurrence of cancer. In this study, Comparison of five different algorithms i.e. Voted Perceptron, LWL, DECORATE, Random Forest and RIDOR is presented. The main aim of this study is to evaluate the performance of those five algorithms for different cancer datasets with different dimensions. The datasets used for the study are chosen such a way that they differ in size, mainly in the terms of number of instances and number of attributes. When comparing the performance of all five algorithms, Random Forest is found to be the better algorithm in most of the cases. |
URI: | https://elibrary.tucl.edu.np/handle/123456789/10660 |
Appears in Collections: | Computer Science & Information Technology |
Files in This Item:
File | Description | Size | Format | |
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Full Thesis.pdf | 1.1 MB | Adobe PDF | View/Open |
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