Browsing by Author "Jiang, MF"

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A framework of features selection for the case-based reasoning
(2000)
Chen, WC
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Tseng, SS
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Chen, JH
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Jiang, MF
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資訊工程學系
CBR is a problem solving technique that reuses past cases and experiences to find a solution to the problems. A critical issue in case-based reasoning is to select the correct and enough features to represent a case. For this reason, the analysis of cases and extraction the necessary features to represent a case are highly recommended in building a CBR system. However, this task is difficult to carry out since such knowledge often cannot be successfully and exhaustively captured and represented. In this paper, a framework of features mining system for the case-based reasoning including two phases has been proposed. The techniques of features selection, data analysis and machine learning can thus be effectively integrated. This will promote flexibility and expandability of case-based reasoning system.
A fuzzy inductive learning algorithm for parallel loop scheduling
(1997)
Tsai, CJ
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Tseng, SS
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Wang, CH
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Yang, CT
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Jiang, MF
The conventional symbolic learning algorithm can not infer data that contains fuzzy information. In the past few years, we have designed a parallel loop scheduling based upon knowledge based approach that is called KPLS to choose an appropriate schedule for different loop to assign loop iterations to a multiprocessor system for achieving high speedup rates. Unfortunately, we found that these attributes that were applied in KPLS contain some fuzzy information, which are inapplicable to the traditional symbolic learning strategy for inferring some concept descriptions. In this paper, we apply fuzzy set concept to AQR learning algorithm that is called FAQR. FAQR which can induce fuzzy linguistic rules from fuzzy instances is then proposed to solve the above parallel loop scheduling problem. Some promising inference rules have been found and applied to infer the choice of parallel loop scheduling. Besides, we apply FAQR in IRIS Flower Classification Problem. Experimental results show that our method yields high accuracy in both different domains.
A parallelized indexing method for large-scale case-based reasoning
(2002-08-01)
Chen, WC
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Tseng, SS
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Chang, LP
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Hong, TP
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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.
Data types generalization and transformation for data mining algorithms
(2000-06-01)
Jiang, MF
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Tseng, SS
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Liao, SY
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資訊工程學系
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Department of Computer Science
In most applications, knowing the characteristics of the data mining methods is usually the key factor to determine the kind of data mining methods should be applied. In this work, we first survey and analyze six kinds of data mining algorithms and four kinds of data forms, and then find out the relations between the mining algorithms and the data types. According to the relations, the suitable generalized data types will be created and is then used to transform the generalized data types of data sources to the suitable one for the selected mining algorithm. With the proposed preprocessing work for data mining algorithms, users can select appropriate mining algorithm just for the goal of application without considering the data types. Finally. two applications for e-mail management and e-mail log analysis are proposed to show the practicality of our method.
Developing a sugar-cane breeding assistant system by a hybrid adaptive learning technique
(1996)
Jiang, MF
;
Wang, CH
;
Tseng, SS
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交大名義發表
;
資訊工程學系
The traditional sugar-cane breeding process depends on the determination of experienced breeding researcher. It's well known that computer-aided methodology is very useful in many fields today, such as CAD, CAM, and CAI. Since the sugar-cane breeding problem in agriculture field is complex, it seems that using computer-aided methodology is very suitable to solve this problem. In this paper, we use the techniques of neural networks and genetic algorithms to construct a method in order to induce the sugar-cane cross model from the sugar-cane parent database which established by Taiwan Sugar Research Institute (TSRI) since 1990. The experimental results show that the correct percentage for testing is about 70%.
Genetic algorithm for extended cell assignment problem in wireless ATM network
(2000)
Din, DR
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Tseng, SS
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Jiang, MF
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資訊工程學系
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Department of Computer Science
In this paper, we investigate the extended cell assignment problem which optimally assigns new and split cells in PCS (Personal Communication Service) to switches in a wireless ATM network. Given cells and switches in an ATM network (whose locations are fixed and known), the problem is assigning cells to switches in an optimum manner. We would like to do the assignment in as attempt to minimize a cost criterion. The cost has two components: one is the cost of handoffs that involve two switches, and the other is the cost of cabling. This problem is modeled as a complex integer programming problem and finding an optimal solution to this problem is NP-complete. A stochastic search method, based on a genetic approach is proposed to solve this problem. Simulation results show that genetic algorithm is robust for this problem.
Intelligent query agent for structural document databases
(1999-08-01)
Jiang, MF
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Tseng, SS
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Tsai, CJ
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資訊工程學系
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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.
Structural cases representation in case-based expert systems
(2000)
Jiang, MF
;
Tseng, SS
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資訊工程學系
;
Department of Computer Science
In recent years, a lot of case-based expert systems (CBES) have been proposed to solve problems in many domains, bur how to effectively retrieve approximate case is still a bottleneck in building CBES. In this work, the embedded relationships between the cases are used to solve the above problem. To elicit the knowledge embedded in the case structure, a new knowledge representation named Structural Cases (SC) which is the basis of our system, is proposed to describe the relationships between two different cases. Based upon this representation, a process of transforming the cases into a set of structural cases is proposed, where two cases with similarity greater than the given threshold are merged into one structural case. Moreover the algorithms for interaction process based on the Structural Cases is also proposed which can be used to retrieve more suitable results. Finally, some examples are given to demonstrate the practicality of the structural cases.
Two-phase clustering process for outliers detection
(2001-05-01)
Jiang, MF
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Tseng, SS
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Su, CM
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資訊工程學系
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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.
Two-phase data types transformation framework in data mining
(2001)
Jiang, MF
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Tseng, SS
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Liao, SY
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Chen, WC
;
資訊工程學系
As we know, the data processed in data mining may be obtained from many sources in which different data types may be used. However, no algorithm can be applied to all applications due to the difficulty for fitting data types of the algorithm, so the selection of an appropriate mining algorithm is based on not only the goal of application, but also the data fittability. Therefore, transforming the non-fitting data type into target one is also an important work in data mining, but the work is often tedious or complex since a lot of data types exist in real world. Merging the similar data types of a given selected mining algorithm into a generalized data type seems to be a good approach to reduce the transformation complexity. In this work, a two-phase data types transformation framework including merging and transforming phases is proposed. With the data type transformation framework, the user can select appropriate mining algorithm iterative and interactive for the goal of application without considering the data types.