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

مُصنِّف ثنائي دقيق وسهل التفسير قائم على قواعد الارتباط باستخدام شدة التضمين وتصويت الأغلبية

Clicks: 121
ID: 265641
2021
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Abstract
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.
في التعلّم الخاضع للإشراف، تتراوح المصنّفات بين نماذج أكثر بساطةً وقابليةً للتفسير وأقل دقةً عمومًا (مثل: CART، وC4.5، وJ48)، ونماذج أكثر تعقيدًا وأقل قابليةً للتفسير وأعلى دقةً (مثل: الشبكات العصبية، وSVM). وفي ظل هذه المفاضلة بين قابلية التفسير والدقة، نقترح مصنّفًا جديدًا قائمًا على قواعد الارتباط؛ أي أنه يجمع بين سهولة التفسير وتحقيق دقة ملائمة. ولتوضيح هذا المقترح، قُورن أداؤه بطرائق أخرى واسعة الاستخدام على ست مجموعات بيانات مفتوحة الوصول.
Reference Key
ghanem2021mathematicsan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Souhila Ghanem, Raphaël Couturier, Pablo Gregori, Souhila Ghanem, Raphaël Couturier, Pablo Gregori
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Journal Mathematics
Year 2021
DOI
10.3390/math9121315
URL
https://www.mdpi.com/2227-7390/9/12/1315/htm
Keywords
classification association rules open access datasets statistical implicative analysis

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