An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote

Autor:	Ghanem, Souhila, Couturier, Raphaël, Gregori, Pablo
Rok vydání:	2021
Předmět:	association rules ComputingMethodologies_PATTERNRECOGNITION classification open access datasets QA1-939 Statistical implicative analysis Open access datasets Classification Association rules Mathematics statistical implicative analysis
Zdroj:	Repositori Universitat Jaume I Universitat Jaume I Mathematics, Vol 9, Iss 1315, p 1315 (2021) Mathematics Volume 9 Issue 12
Popis:	In supervised learning, classifiers range from simpler, more interpretable and generally less accurate ones (e.g., CART, C4.5, J48) to more complex, less interpretable and more accurate ones (e.g., neural networks, SVM). In this tradeoff between interpretability and accuracy, we propose a new classifier based on association rules, that is to say, both easy to interpret and leading to relevant accuracy. To illustrate this proposal, its performance is compared to other widely used methods on six open access datasets.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::643c8ca2e5e7ecfb04282c303ad3f2f2 http://hdl.handle.net/10234/194101 Zobrazit plný text záznamu