CAIM discretization algorithm

By Lukasz Kurgan, Krzysztof Jozef Cios. IEEE Transactions on Knowledge and Data Engineering, 2004

460 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/573d42695ea8311c39487e0b9ba21c6f3320bead

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Citations in the corpus

Learning Complexity-Bounded Rule-Based Classifiers by Combining. Association Analysis and Genetic Algorithms (2005, methodology): “After having examined many of these methods, we found that the CAIM approach recently proposed in [9] is a favorable choice for our application.”
A Provable Algorithm for Learning Interpretable Scoring Systems (2018, methodology): “We also test the performance of models where we perform data discretization as the pre-processing step using top-down discretization methods such as CAIM [15], CACC [26], Ameva [13], and the Minimum Description Length Principle method [8].”
EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect (2012, background): “Use of patterns in the data itself has been shown to improve classiﬁer performance [24].”
A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning (2013, unknown): “Finally, we present the taxonomy.”
Ltd Registered in England and Wales Registered Number : 1072954 Registered office : (2004, methodology): “ChiMerge (Kerber 1992), Chi2 (Liu and Setiono 1997), Modified Chi2 (Tay and Shen 2002), StatDisc (Richeldi and Rossotto 1995), and Khiops (Boulle 2004); class-attribute interdependence algorithms, such as CAIM (Kurgan and Cios 2004) and CADD (Ching, Wong, and Chan 1995); and clustering-based algorit”
A review of epileptic seizure detection using machine learning classifiers (2020, methodology): “The most popular training approach is tenfold cross-validation [55], where each fold, i.e., one horizontal segment of the dataset is considered to be the testing dataset and the remaining nine segments are used as the training dataset [56, 57].”
Discretization Techniques: A recent survey (2006, methodology): “The goal of the CAIM algorithm [26] is to maximize the class-attribute interdependence and to generate a (possibly) minimal number of discrete intervals.”