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A Comparison of Open Source Data Mining Tools for Breast Cancer Classification
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Author(s): Ahmed AbdElhafeez Ibrahim (Arab Academy for Science, Technology, and Maritime Transport, Egypt), Atallah Ibrahin Hashad (Arab Academy for Science, Technology, and Maritime Transport, Egypt)and Negm Eldin Mohamed Shawky (Arab Academy for Science, Technology, and Maritime Transport, Egypt)
Copyright: 2017
Pages: 16
Source title:
Handbook of Research on Machine Learning Innovations and Trends
Source Author(s)/Editor(s): Aboul Ella Hassanien (Cairo University, Egypt)and Tarek Gaber (Suez Canal University, Egypt)
DOI: 10.4018/978-1-5225-2229-4.ch027
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Abstract
Data Mining is a field that interconnects areas from computer science, trying to discover knowledge from databases in order to simplify the decision making. Classification is a Data Mining chore that learns from a set of instances in order to precisely classify the target class for new instances. Open source Data Mining tools can be used to make classification. This paper compares four tools: KNIME, Orange, Tanagra and Weka. Our goal is to discover the most precise tool and technique for breast cancer classifications. The experimental results show that some tools achieve better results more than others. Also, using fusion classification task verified to be better than the single classification task over the four datasets have been used. Also, we present a comparison between using complete datasets by substituting missing feature values and incomplete ones. The experimental results show that some datasets have better accuracy when using complete datasets.
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