Othman, Mohd. Fauzi and Moh, Thomas Shan Yau (2007) Comparison of different classification techniques using WEKA for breast cancer. In: Proceedings of the Int Federat Med & Biol Engn. Springer, New York, United States, pp. 520-523. ISBN 978-3-540-68016-1
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The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. In this paper present the comparison of different classification techniques using Waikato Environment for Knowledge Analysis or in short, WEKA. WEKA is an open source software which consists of a collection of machine learning algorithms for data mining tasks. The aim of this paper is to investigate the performance of different classification or clustering methods for a set of large data. The algorithm or methods tested are Bayes Network, Radial Basis Function, Pruned Tree, Single Conjunctive Rule Learner and Nearest Neighbors Algorithm. A fundamental review on the selected technique is presented for introduction purposes. The data breast cancer data with a total data of 6291 and a dimension of 699 rows and 9 columns will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.
|Item Type:||Book Section|
|Additional Information:||3rd Kuala Lumpur International Conference on Biomedical Engineering 2006 (BioMed 2006), Kuala Lumpur, Malaysia, Dec. 11-14, 2006|
|Uncontrolled Keywords:||machine learning, data mining, WEKA, classification, bioinformatics|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Deposited By:||Nor Asmida Abdullah|
|Deposited On:||28 Dec 2010 08:00|
|Last Modified:||28 Dec 2010 08:00|
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