 MIL: a data discretisation approachBikash Kanti Sarkar, Shib Sankar Sana and Kripasindhu Chaudhuri International Journal of Data Mining, Modelling and Management, 2011, vol. 3, issue 3, 303-318 Abstract: Data discretisation is an important step in the process of machine learning, since it is easier for classifiers to deal with discrete attributes rather than continuous attributes. Over the years, several methods of performing discretisation such as Boolean reasoning, equal frequency binning, entropy have been proposed, explored, and implemented. In this article, a simple supervised discretisation approach called minimum information loss (MIL) is introduced. The prime goal of MIL is to maximise classification accuracy of classifier, minimising loss of information while discretisation of continuous attributes. The performance of the suggested approach is compared with the supervised discretisation algorithms: selective pseudo iterative deletion 4.7 (SPID4.7) and minimum description length principle (MDLP), using four state-of-the-art rule inductive algorithms – neural network, C4.5, Naive-Bayes, and CN2. The empirical results show that the presented approach performs better in several cases in comparison to the other two algorithms. Keywords: data mining; data discretisation; classifiers; accuracy; information loss; machine learning. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:3:y:2011:i:3:p:303-318 Access Statistics for this article More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd |
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