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Automatically integrating multiple rule sets in a distributed-knowledge environment
(1998-08-01)
Wang, CH
;
Hong, TP
;
Tseng, SS
;
Liao, CM
;
資訊工程學系
In this paper, an actual knowledge application is made by means of evolution paradigms in terms of knowledge acquisition. rin automatic knowledge integration approach in a distributed-knowledge environment is thus proposed to integrate multiple rule sets into a single effective rule set. The proposed approach consists of two phases: knowledge encoding and knowledge integration. In the encoding phase, each knowledge input is translated and expressed as a rule set, then encoded as a bit string. The combined bit strings from multiple knowledge inputs form an initial knowledge population, which is then ready for integration. In the knowledge integration phase, a genetic search technique generates an optimal or nearly optimal rule set from these initial knowledge-input strings. Finally, experimental results from diagnosis of brain tumors show that the rule set derived by the proposed approach is much more accurate than each initial rule set.
A fuzzy inductive learning strategy for modular rules
(1999-04-01)
Wang, CH
;
Liu, JF
;
Hong, TP
;
Tseng, SS
;
資訊工程學系
In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. The design of learning methods to learn concept descriptions in working with vague data is thus very important. In this paper, we apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the maximum information gain is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy rules from "soft" instances, which differ from conventional instances in that they have class membership values. Experiments on the Sports and the Iris Flower classification problems are presented to compare the accuracy of the proposed algorithm with those of some other learning algorithms. Experimental results show that the rules derived from our approach are simpler and yield higher accuracy than those from some other learning algorithms. (C) 1999 Elsevier Science B.V. All rights reserved.
Splitting and merging version spaces to learn disjunctive concepts
(1999-09-01)
Hong, TP
;
Tseng, SS
;
資訊工程學系
;
Department of Computer Science
We have modified the original version space strategy in order to learn disjunctive concepts incrementally and without saving past training instances. The algorithm time complexity is also analyzed, and its correctness is proven.
Integrating membership functions and fuzzy rule sets from multiple knowledge sources
(2000-05-16)
Wang, CH
;
Hong, TP
;
Tseng, SS
;
資訊工程學系
;
Department of Computer Science
Ln this paper, we propose a GA-based fuzzy knowledge-integration framework that can simultaneously integrate multiple fuzzy rule sets and their membership function sets. The proposed two-phase approach includes fuzzy knowledge encoding and fuzzy knowledge integration. In the encoding phase, each fuzzy rule set with its associated membership functions is first transformed into an intermediary representation, and further encoded as a string. The combined strings form an initial knowledge population which is then ready for integration. In the knowledge-integration phase. a genetic algorithm is used to generate an optimal or nearly optimal set of fuzzy rules and membership functions from the initial knowledge population. The hepatitis diagnostic problem was used to show the performance of the proposed knowledge-integration approach. Results show that the fuzzy knowledge-base resulting from using our approach performs better than every individual knowledge base. (C) 2000 Elsevier Science B.V. All rights reserved.
A coverage-based genetic knowledge-integration strategy
(2000-07-01)
Wang, CH
;
Hong, TP
;
Chang, MB
;
Tseng, SS
;
資訊工程學系
In this paper, we propose a coverage-based genetic knowledge-integration approach to effectively integrate multiple rule sets into a centralized knowledge base. The proposed approach consists of two phases: knowledge encoding and knowledge integration. In the knowledge-encoding phase, each rule in the various rule sets that are derived from different sources (such as expert knowledge or existing knowledge bases) is first translated and encoded as a fixed-length bit string. The bit strings combined together thus form an initial knowledge population. In the knowledge-integration phase, a genetic algorithm applies genetic operations and credit assignment at each rule-string to generate an optimal or nearly optimal rule set. Experiments on diagnosing brain tumors were made to compare the accuracy of a rule set generated by the proposed approach with that of the initial rule sets derived from different groups of experts or induced by various machine learning techniques. Results show that the rule set derived by the proposed approach is more accurate than each initial rule set on its own. (C) 2000 Elsevier Science Ltd. All rights reserved.
A genetics-based approach to knowledge integration and refinement
(2001-01-01)
Wang, CH
;
Hong, TP
;
Tseng, SS
;
資訊工程學系
;
Department of Computer Science
In this paper, we propose a genetics-based knowledge integration approach to integrate multiple rule sets into a central rule set. The proposed approach consists of two phases: knowledge encoding and knowledge integrating. In the encoding phase, each knowledge input is translated and expressed as a rule set, and then encoded as a bit string. The combined bit strings form an initial knowledge population, which is then ready for integrating. In the knowledge integration phase, a genetic algorithm generates an optimal or nearly optimal rule set from these initial knowledge inputs. Furthermore, a rule-refinement scheme is proposed to refine inference rules via interaction with the environment. Experiments on diagnosing brain tumors were carried out to compare the accuracy of a rule set generated by the proposed approach with that of initial rule sets derived from different groups of experts or induced by means of various machine learning techniques. Results show that the rule set derived using the proposed approach is much more accurate than each initial rule set on its own.
Learning concepts by arranging appropriate training order
(2001-08-01)
Hsu, YT
;
Hong, TP
;
Tseng, SS
;
資訊工程學系
;
Department of Computer Science
Machine learning has been proven useful for solving the bottlenecks in building expert systems. Noise in the training instances will, however, confuse a learning mechanism. Two main steps are adopted here to solve this problem. The first step is to appropriately arrange the training order of the instances. It is well known from Psychology that different orders of presentation of the same set of training instances to a human may cause different learning results. This idea is used here for machine learning and an order arrangement scheme is proposed. The second step is to modify a conventional noise-free learning algorithm, thus making it suitable for noisy environment. The generalized version space learning algorithm is then adopted to process the training instances for deriving good concepts. Finally, experiments on the Iris Flower problem show that the new scheme can produce a good training order, allowing the generalized version space algorithm to have a satisfactory learning result.
A parallelized indexing method for large-scale case-based reasoning
(2002-08-01)
Chen, WC
;
Tseng, SS
;
Chang, LP
;
Hong, TP
;
Jiang, MF
Case-based reasoning (CBR) is a problem-solving methodology commonly seen in artificial intelligence. It can correctly take advantage of the situations and methods in former cases to find out suitable solutions for new problems. CBR must accurately retrieve similar prior cases for getting a good performance. In the past, many researchers proposed useful technologies to handle this problem. However, the performance of retrieving similar cases may be greatly influenced by the number of cases. In this paper, the performance issue of large-scale CBR is discussed and a parallelized indexing architecture is then proposed for efficiently retrieving similar cases in large-scale CBR. Several algorithms for implementing the proposed architecture are also described. Some experiments are made and the results show the efficiency of the proposed method. (C) 2002 Elsevier Science Ltd. All rights reserved.
Fuzzy inductive learning strategies
(2003-03-01)
Wang, CH
;
Tsai, CR
;
Hong, TP
;
Tseng, SS
;
資訊工程學系
In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. Design of learning methods for working with vague data is thus very important. In this paper, we apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the AQR learning strategy is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy linguistic rules from "soft" instances. Experiments on the Sports and the Iris Flower classification problems are presented to compare the accuracy of the proposed algorithm with those of some other learning algorithms. Experimental results show that the rules derived from our approach are simpler and yield higher accuracy than those from some other learning algorithms.
Flexible online association rule mining based on multidimensional pattern relations
(2006-06-22)
Wang, CY
;
Tseng, SS
;
Hong, TP
;
資訊工程學系
;
Department of Computer Science
Most incremental mining and online mining algorithms concentrate on finding association rules or patterns consistent with entire current sets of data. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. To provide ad-hoc, query-driven, and online mining support, we first propose a relation called the multidimensional pattern relation to structurally and systematically store context and mining information for later analysis. Each tuple in the relation comes from an inserted dataset in the database. We then develop an online mining approach called three-phase online association rule mining (TOARM) based on this proposed multidimensional pattern relation to support online generation of association rules under multidimensional considerations. The TOARM approach consists of three phases during which final sets of patterns satisfying various mining requests are found. It first selects and integrates related mining information in the multidimensional pattern relation, and then if necessary, re-processes itemsets without sufficient information against the underlying datasets. Some implementation considerations for the algorithm are also stated in detail. Experiments on homogeneous and heterogeneous datasets were made and the results show the effectiveness of the proposed approach. (c) 2005 Elsevier Inc. All rights reserved.

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