Data-driven generation of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization

اسناد مشابه

• Data-driven generation of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization

  مقاله‌ی دسترسی آزاد۲۰۰۳International Journal of Approximate ReasoningJanosAbonyi, Johannes A.Roubos, FerencSzeifert

• Data-Driven Discovery of Protein Function Classifiers: Decision Trees Based on MEME Motifs outperform PROSITE Patterns and Profiles on Peptidase Families

  سند علمی۲۰۰۲JCISXiangyun Wang, Diane Schroeder, Drena Dobbs, Vasant Honavar

  This paper describes an approach to data-driven discovery of decision trees or rules for assigning protein sequences to functional families using sequence motifs. This method is able to capture regularities that can be described in terms of presence or absence of arbitrary combin

• Generation of Attribute Value Taxonomies from Data and Their Use in Data-Driven Construction of Accurate and Compact Naive Bayes Classifiers

  سند علمی۲۰۰۴Dae-Ki Kang, Adrian Silvescu, Jun Zhang, Vasant Honavar

  Attribute Value Taxonomies (AVT) have been shown to be useful in constructing compact and robust classifiers. However, in many application domains, human-designed AVTs are unavailable. For this problem, we introduce AVT-Learner, an algorithm for automated construction of attribut

• Data-Driven Generation of Decision Trees for Motif-Based Assignment of Protein Sequences to Functional Families

  سند علمی۲۰۰۰Dake Wang, Xiangyun Wang, Vasant Honavar, Drena L. Dobbs

  This paper describes an approach to data-driven discovery of sequence motif-based models in the form of decision trees for assigning protein sequences to functional families. Unlike approaches that try to classify protein sequences based on presence of a single motif, this method

• Supervised Clustering and Fuzzy Decision Tree Induction for the Identification of Compact Classifiers

  سند علمی۲۰۰۴Ferenc Peter Pach, Janos Abonyi, Peter Arva

  Fuzzy decision tree induction algorithms require the fuzzy quantization of the input variables. This paper demonstrates that supervised fuzzy clustering combined with similarity-based rule-simplification algorithms is an effective tool to obtain the fuzzy quantization of the inpu

• Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: generation and evaluation

  سند علمی۲۰۰۲IEEE Trans. Systems, Man, and Cybernetics, Part CSushmita Mitra, Kishori M. Konwar, Sankar K. Pal

  A fuzzy knowledge-based network is developed based on the linguistic rules extracted from a fuzzy decision tree. A scheme for automatic linguistic discretization of continuous attributes, based on quantiles, is formulated. A novel concept for measuring the goodness of a decision