Abstract
In this paper, an Enhanced Class-Attribute Interdependence Maximization discretization algorithm (ECAIM) is proposed by 2 extensions to improve a state-of-the-art Class-Attribute Interdependence Maximization discretization algorithm (CAIM). The main drawback that remains unresolved in CAIM is that its stopping criterion depends on the number of target classes. When the number of target classes is large, its performance drops, as CAIM is not a real incremental discretization method. The first extension, ECAIM is extended from CAIM to become a real incremental discretization method by improving the stopping criterion. The stopping criterion is based on the Slope of an ecaim value which decreases with an increasing number of intervals. If the slope of ecaim value is less than the specified threshold then the discretization terminates. The second extension that we propose is the multi-attribute techniques by simultaneously considering all attributes instead of a single-attribute like CAIM, for accurate and efficient discretizers solution. ECAIM use a feature selection algorithm to select a subset of attributes for reducing the number of attributes, remove irrelevant, redundant attributes and then use multi-attribute techniques only on this subset attributes. Experiment results on 15 real-world datasets show that ECAIM is more efficient than CAIM in terms of accuracy, number of intervals and number of generated rules.
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Sriwanna, K., Puntumapon, K., Waiyamai, K. (2012). An Enhanced Class-Attribute Interdependence Maximization Discretization Algorithm. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_39
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DOI: https://doi.org/10.1007/978-3-642-35527-1_39
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