Research Article
Predicting Primary Tumors using Multiclass Classifier Approach of Data Mining
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 96 - Issue 8 |
| Published: June 2014 |
| Authors: Mehak Naib, Amit Chhabra |
10.5120/16813-6559
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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 },
year = { 2014 },
volume = { 96 },
number = { 8 },
pages = { 9-13 },
doi = { 10.5120/16813-6559 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A Mehak Naib
%A Amit Chhabra
%T Predicting Primary Tumors using Multiclass Classifier Approach of Data Mining%T
%J International Journal of Computer Applications
%V 96
%N 8
%P 9-13
%R 10.5120/16813-6559
%I Foundation of Computer Science (FCS), NY, USA
Abstract
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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