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Predicting Primary Tumors using Multiclass Classifier Approach of Data Mining

by Mehak Naib, Amit Chhabra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 8
Year of Publication: 2014
Authors: Mehak Naib, Amit Chhabra

Mehak Naib, Amit Chhabra . Predicting Primary Tumors using Multiclass Classifier Approach of Data Mining. International Journal of Computer Applications. 96, 8 ( June 2014), 9-13. DOI=10.5120/16813-6559

@article{ 10.5120/16813-6559,
author = { Mehak Naib, Amit Chhabra },
title = { Predicting Primary Tumors using Multiclass Classifier Approach of Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 8 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { },
doi = { 10.5120/16813-6559 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:21:12.144291+05:30
%A Mehak Naib
%A Amit Chhabra
%T Predicting Primary Tumors using Multiclass Classifier Approach of Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 8
%P 9-13
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

Data mining has been widely adopted in recent years in many fields, especially in the medical field. This paper highlights the prediction of unknown primary tumors in the dataset. The multiclass classifier with Random forest is used for classification of multiclass dataset as it gives much higher accuracy than binary classifiers. SMOTE method for this imbalanced dataset with Randomize technique is applied during preprocessing for reducing the biasness among classes. These all evaluations and results are carried out using WEKA 3. 6. 10 as a data mining tool.

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Index Terms

Computer Science
Information Sciences


SMOTE WEKA Primary tumor Multiclass classifier Random forest