Abstract
Data mining is about extracting useful knowledge from data. It has various techniques and algorithms. Yet, the most widely used are classification algorithms which deal with the problem of affecting new data element to one of predefined classes. There are a wide range of classification algorithms such as decision trees, neural networks, K-NN, Bayes, support vector machines (SVM); and so on. This study focuses on four algorithms; Naive Bayes, Multi-Layer Perceptron (MLP), SVM and C4.5; all of them are based on mathematical calculations but in different ways. In this paper, we aim to make a comparison between the four algorithms in terms of well-chosen criteria like classification accuracy and execution time. Moreover, we implement these algorithms with the same dataset; relative to diabetes on women; in order to present the different results by using Waikato environment for knowledge analysis (Weka).
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Sossi Alaoui, S., Farhaoui, Y., Aksasse, B. (2018). A Comparative Study of the Four Well-Known Classification Algorithms in Data Mining. In: Ezziyyani, M., Bahaj, M., Khoukhi, F. (eds) Advanced Information Technology, Services and Systems. AIT2S 2017. Lecture Notes in Networks and Systems, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-69137-4_32
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DOI: https://doi.org/10.1007/978-3-319-69137-4_32
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