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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.
Intelligent query agent for structural document databases
(1999-08-01)
Jiang, MF
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Tseng, SS
;
Tsai, CJ
;
資訊工程學系
;
Department of Computer Science
Querying a database for document retrieval is often a process close to querying an answering expert system. In this work, we apply the expert system techniques to the intelligent query agent establishment and regard the structural document database as the expertise which can be the objective of the knowledge acquisition. A new knowledge representation, named Structural Documents (SDs), is proposed to be the base of our model, and a transformation process from the raw data to the format of a database is applied. Based on the SDs, more suitable results could be inferenced rapidly by inference engine, and the flow of inference is also described. For implementation, an intelligent Chinese information retrieval system for personnel regulations by integrating knowledge-based and full-text searching techniques is proposed. In our experiments, the structural information of the documents can be acquired from the database using the knowledge extraction module. By observing the operating process of users, we found the query process of users are simplified. (C) 1999 Published by Elsevier Science Ltd. 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.
An intelligent parallel loop scheduling for parallelizing compilers
(2000-03-01)
Fann, YW
;
Yang, CT
;
Tseng, SS
;
Tsai, CJ
;
資訊工程學系
In this paper we propose a knowledge-based approach to solving loop-scheduling problems. A rule-based system, called IPLS, is developed by combining a repertory grid and an attribute ordering table to construct a knowledge base. IPLS chooses an appropriate scheduling algorithm by inferring some features of loops and assigning parallel loops to multiprocessors to achieve significant speedup. Because more attributes are proposed, the accuracy of selection of an appropriate scheduling method is improved. In addition, the refined IPLS system can automatically adjust the attributes in the knowledge base according to profile information; therefore, IPLS has the capability of feedback learning. The experimental results show that our approach can achieve greater speedup on multiprocessor systems than can others.
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.
Using knowledge-based systems for research on parallelizing compilers
(2001-03-01)
Yang, CT
;
Tseng, SS
;
Fann, YW
;
Tsai, TK
;
Hsieh, MH
The main function of parallelizing compilers is to analyze sequential programs, in particular the loop structure, to detect hidden parallelism and automatically restructure sequential programs into parallel subtasks that are executed on a multiprocessor. This article describes the design and implementation of an efficient parallelizing compiler to parallelize loops and achieve high speedup rates on multiprocessor systems. It is well known that the execution efficiency of a loop can be enhanced if the loop is executed in parallel or partially parallel, such as in a DOALL or DOACROSS loop. This article also reviews a practical parallel loop detector (PPD) that is implemented in our PFPC on finding the parallelism in loops. The PPD can extract the potential DOALL and DOACROSS loops in a program by verifying array subscripts. In addition, a new model by using knowledge-based approach is proposed to exploit more loop parallelisms in this paper. The knowledge-based approach integrates existing loop transformations and loop scheduling algorithms to make good use of their ability to extract loop parallelisms. Two rule-based systems, called the KPLT and IPLS, are then developed using repertory grid analysis and attribute-ordering tables respectively, to construct the knowledge bases. These systems can choose an appropriate transform and loop schedule, and then apply the resulting methods to perform loop parallelization and obtain a high speedup rate. For example, the IPLS system can choose an appropriate loop schedule for running on multiprocessor systems. Finally, a runtime technique based on the inspector/executor scheme is proposed in this article for finding available parallelism on loops. Our inspector can determine the wavefronts of a loop with any complex indirected array-indexing pattern by building a DEF-USE table. The inspector is fully parallel without any synchronization. Experimental results show that the new method can resolve any complex data dependence patterns where no previous research can. One of the ultimate goals is to construct a high-performance and portable FORTRAN parallelizing compiler on shared-memory multiprocessors. We believe that our research may provide more insight into the development of a highperformance parallelizing compiler Copyright (C) 2001 John Wiley & Sons, Ltd.
Two-phase clustering process for outliers detection
(2001-05-01)
Jiang, MF
;
Tseng, SS
;
Su, CM
;
資訊工程學系
;
Department of Computer Science
In this paper, a two-phase clustering algorithm for outliers detection is proposed. Tn;e first modify the traditional k-means algorithm in Phase 1 by using a heuristic "if one new input pattern is far enough away from all clusters centers, then assign it as a new cluster center". It results that the data points in the same cluster may be most likely all outliers or all non-outliers. And then we construct a minimum spanning tree (MST) in Phase 2 and remove the longest edge. The small clusters, the tree with less number of nodes, are selected and regarded as outlier. The experimental results show that our process works well. (C) 2001 Elsevier Science B.V. All rights reserved.

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