Probabilistic Distance Measures for Prototype-based Rules
نویسندگان
چکیده
Probabilistic distance functions, including several variants of value difference metrics, minimum risk metric and ShortFukunaga metrics, are used with prototype-based rules (P-rules) to provide a very concise and comprehensible classification model. Application of probabilistic metrics to nominal or discrete features is straightforward. Heterogeneous metrics that handle continuous attributes with discretized or interpolated probabilistic metrics were combined with several methods of probability density estimation. Numerical experiments on artificial and real data show the usefulness of such approach as an alternative to neurofuzzy models.
منابع مشابه
Fuzzy rule-based systems
Relations between similarity-based systems, evaluating similarity to some prototypes, and fuzzy rule-based systems, aggregating values of membership functions, are investigated. Similarity measures based on information theory and probabilistic distance functions lead to a new type of membership functions applicable to symbolic data. Fuzzy membership functions on the other hand lead to a new typ...
متن کاملGeneric probabilistic prototype based classification of vectorial and proximity data
In supervised learning probabilistic models are attractive to define discriminative models in a rigid mathematical framework. More recently, prototype approaches, known for compact and efficient models, were defined in a probabilistic setting, but are limited to metric vectorial spaces. Here we propose a generalization of the discriminative probabilistic prototype learning algorithm for arbitra...
متن کاملInconsistency measures for probabilistic logics
Inconsistencies in knowledge bases are of major concern in knowledge representation and reasoning. In formalisms that employ model-based reasoning mechanisms inconsistencies render a knowledge base useless due to the non-existence of a model. In order to restore consistency an analysis and understanding of inconsistencies is mandatory. Recently, the field of inconsistency measurement has gained...
متن کاملPrototype Rules from SVM
1 Why prototype-based rules? Propositional logical rules may not be the best way to understand the class structure of data describing some objects or states of nature. The best explanation may differ depending on the problem, the type of questions and the type of explanations that are commonly accepted in a given field. Although most research has focused on propositional logical rules [14, 19] ...
متن کاملHeterogeneous distance functions for prototype rules: influence of parameters on probability estimation
An interesting and little explored way to understand data is based on prototype rules (P-rules). The goal of this approach is to find optimal similarity (or distance) functions and position of prototypes to which unknown vectors are compared. In real applications similarity functions frequently involve different types of attributes, such as continuous, discrete, binary or nominal. Heterogeneous...
متن کامل