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A fuzzy theory refinement algorithm composed of a heuristic process of generalization, specification, addition and elimination of rules is proposed. ... The refinement module appended to SLAVE (SLAVE + R) is proposed as a method for verifying that the union of the partial solutions is a good global solution. ... Therefore, given an incomplete and/or incorrect fuzzy rule base (fuzzy theory) that represents the initial knowledge (where this set can be empty) and a set of consistent examples, the refinement problem ...doi:10.1016/s0888-613x(98)00013-9 fatcat:qaxilp7i2zb5xm7gl7w7dlb56i
The author is using upward refinement for solving the HF problem as follows: if a background theory B is empty, then every correct hypothesis H can be obtained by applying upward refinement to a given ... The paper proves that when both B and E are clausal theories such that BUE, a clausal theory H satisfies B \ HOE if and only if H is obtained by this general method of upward refinement applied to residue ...
His definition of a so-called fuzzy theory is motivated “by providing the minimal machinery to interpret loop-free schemes in a fuzzy way”. ... The fuzzy specification of atomic program pieces (with respect to a fuzzy theory T) as functions from a set X (of states) into TX (vague specifications of output states) and the corresponding fuzzy logic ...
Therefore, in Phase 1, we apply Fuzzy Set Theory to transform the numeric testing records of learners into symbolic data, apply Education Theory to further refine it, and apply Data Mining approach to ... heuristic algorithm to automatically construct the concept map. ... Tsai et al. (2001) proposed a Two-Phase Fuzzy Mining and Learning Algorithm. ...doi:10.1016/j.compedu.2005.11.020 fatcat:moh4cnmncjd4zexjfx32pj5ple
The acquisition of the information needed to perform the refinements is incorporated into a general algorithm for determining the number of examples and counterexamples of rules with fuzzy temporal constraints ... Fuzzy sets have been used in data mining since the mid 1990s (see  for an overview of early applications of fuzzy set theory in data mining). ...doi:10.1016/j.ijar.2007.06.002 fatcat:ousw74q7e5ec5flbvftwq2ki7e
Benchmark study on a DNA promoter recognition problem shows that with the added advantage of fast learning, cascade ARTMAP rule insertion and refinement algorithms produce performance superior to those ... A rule insertion algorithm translates if-then symbolic rules into cascade ARTMAP architecture. ... ACKNOWLEDGMENT This article is based on a chapter of a doctoral dissertation submitted to Boston University. The author wishes to acknowledge his dissertation committee members, G. A. Carpenter, S. ...doi:10.1109/72.557661 pmid:18255628 fatcat:n5d5cirlqnd2xig6p5cycnxtzy
This study presents a learning performance assessment scheme by combining four computational intelligence theories, i.e., the proposed refined K-means algorithm, the neuro-fuzzy classifier, the proposed ... feature reduction scheme, and fuzzy inference, to identify the learning performance assessment rules using the web-based learning portfolios of an individual learner. ... Feature Reduction Algorithm Refined fuzzy rules Primal fuzzy rules Neuro-Fuzzy Classifier Learner Learning behavior The gathered learning portfolios Refined K-means Algorithm Clustering C 1 C 2 C ...doi:10.1109/ijcnn.2006.247063 dblp:conf/ijcnn/ChenHCL06 fatcat:5ov4p7eq7zgmhmax5ddalg266a
Since both theories originated in the expert system domain, there are a number of research proposals that combine rough and fuzzy concepts in supervised learning. ... However, continuing developments of rough and fuzzy extensions to clustering, neurocomputing, and genetic algorithms make hybrid approaches in these areas a potentially rewarding research opportunity as ... In  , an approach to web query expansion is proposed using fuzzy-rough set theory as a framework for query refinement. ...doi:10.1109/fuzzy.2007.4295352 dblp:conf/fuzzIEEE/LingrasJ07 fatcat:vygojaq46zb6zej6q3jtlna5au
Lecture Notes in Computer Science
In this work, we propose a Two-Phase Fuzzy Mining and Learning Algorithm, integrating data mining algorithm, fuzzy set theory, and look ahead mechanism, to find the embedded information, which can be provided ... to teachers for further analyzing, refining or reorganizing the teaching materials and tests, from historical learning records. ... Our idea is to improve Apriori algorithm by applying fuzzy set theory to overcome the problem of existing fuzzy regions among the data. ...doi:10.1007/3-540-45718-6_47 fatcat:skpd2plffzbj7gt2ct7eehad6i
To solve the problem that the H=α-Wishart unsupervised classification algorithm can generate only inflexible clusters due to arbitrarily fixed zone boundaries in the clustering processing, a refined fuzzy ... To devise a unified fuzzy function, a normalized Wishart distance is proposed during the clustering step in the new algorithm. Then the degree of membership is computed to implement fuzzy clustering. ... Liu et al. used semisupervised learning theory and the core theory of the fuzzy C-means (FCM) algorithm to improve classification accuracy. 16 Obviously, fuzzy theory can be applied to improve the accuracy ...doi:10.1117/1.jrs.9.096098 fatcat:v4zrpqze6reopipo7ap6yetrda
a refiner unit in the TMP process. ... In this research, six evolutionary optimization algorithms are employed to be joined with the adaptive neuro-fuzzy inference system (ANFIS) to increase the refining simulation accuracy. ... In 1960, Holland introduced genetic algorithms based on the evolutionary theory of Darwin. ...doi:10.3390/en13195113 fatcat:l3orm4p3kzcybonoqyj6tpfecm
The concepts of complement of a fuzzy graph and complete fuzzy bigraph mentioned here need further refinement. ... ISBN 3-7908-1286-2 The book under review is a recent entry into the fast growing branch of fuzzy set theory, the theory of fuzzy graphs. ...
This requires the introduction of fuzzy sets. An algorithmic procedure is provided for including default-type constraints into the constraint propagation problem. ... In order to be able to handle these types of imprecise situations we can introduce some ideas from the theory of fuzzy sets (Zadeh  ). ... Assume that V is a variable or node that assumes its value in the set X. A fuzzy label set is a fuzzy subset of X. ...doi:10.1016/0888-613x(89)90022-4 fatcat:26pwe2gn5zbnxi3aoc23fz6klm
(English summary) Fuzzy Sets and Systems 66 (1994), no. 1, 1-13. Summary: “In this paper a fuzzy neural network is a layered, feedforward, neural net that has fuzzy signals and/or fuzzy weights. ... We present a general methodology for designing an efficient algorithm to find a k-mmg. ...
Reddy, On the semantics of refinement calculi (359-374); Pascal Zimmer, Subtyping and typing algorithms for mobile ambients (375-390). ... Giangiacomo Gerla, Closure operators in fuzzy set theory (243-278); Bernadette Bouchon-Meunier and Christophe Marsala, Learning fuzzy decision rules (279-304); Detlef Nauck and Rudolf Kruse, Neuro-fuzzy ...
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