Fuzzy-Relational Classification: Combining Pairwise Decomposition Techniques with Fuzzy Preference Modeling
نویسندگان
چکیده
This paper introduces a new approach to classification which combines pairwise decomposition techniques from machine learning with ideas and tools from fuzzy preference modeling. The approach, called fuzzy relational classification, effectively reduces the problem of classification to a problem of decision making based on a fuzzy preference relation. It will be shown that, by decomposing such a relation into a strict preference, an indifference, and an incomparability relation, it becomes possible to quantify different types of uncertainty in classification, and thereby to support more sophisticated classification and postprocessing strategies.
منابع مشابه
Learning valued preference structures for solving classification problems
This paper introduces a new approach to classification which combines pairwise decomposition techniques with ideas and tools from fuzzy preference modeling. More specifically, our approach first decomposes a polychotomous classification problem involving m classes into an ensemble of binary problems, one for each ordered pair of classes. The corresponding classifiers are trained on the relevant...
متن کاملEnhancing Fuzzy Rule Based Systems in Multi-Classification Using Pairwise Coupling with Preference Relations
This contribution proposes a technique for Fuzzy Rule Based Classification Systems (FRBCSs) based on a multi-classifier approach using fuzzy preference relations for dealing with multi-class classification. The idea is to decompose the original data-set into binary classification problems using a pairwise coupling approach (confronting all pair of classes), and to obtain a fuzzy system for each...
متن کاملSolving multi-class problems with linguistic fuzzy rule based classification systems based on pairwise learning and preference relations
This paper deals with multi-class classification for linguistic fuzzy rule based classification systems. The idea is to decompose the original data-set into binary classification problems using the pairwise learning approach (confronting all pair of classes), and to obtain an independent fuzzy system for each one of them. Along the inference process, each fuzzy rule based classification system ...
متن کاملDeriving priorities from fuzzy pairwise comparison judgements
A new approach for deriving priorities from fuzzy pairwise comparison judgements is proposed, based on -cuts decomposition of the fuzzy judgements into a series of interval comparisons. The assessment of the priorities from the pairwise comparison intervals is formulated as an optimisation problem, maximising the decision-maker’s satisfaction with a speci4c crisp priority vector. A fuzzy prefer...
متن کاملFuzzy Preference Based Route Choice Model
This paper introduces a methodology for route choice based on fuzzy preference relations. The core of the model is FiPV (Fuzzy-individuelle Präferenzen von Verkehrsteilnehmern or fuzzy traveler preferences), that is a choice function based on fuzzy pairwise comparisons for travel decisions. The proposed model may be the first application of fuzzy individual (preference-based) choice in travel d...
متن کامل