Browsing by Author "Chen, AP"

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A three-phase knowledge extraction methodology using learning classifier system
(2005)
Chen, AP
;
Chen, KK
;
Chen, MY
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Machine learning methods. such as fuzzy logic, neural networks and decision tree induction have been applied to learn rules but they may be trapped into local optimal. Based on the principle of natural evolution and global searching, a genetic algorithm is promising in obtaining better results. This article adopts learning classifier systems (LCS) technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. This paper makes three important contributions: (1) it represents various rule sets that are derived from different sources and encoded as a fixed-length bit string in the knowledge encoding phase; (2) it uses three criteria (accuracy, coverage, and fitness) to select an optimal set of rules from a large population in the knowledge extraction phase; (3) it applies genetic operations to generate optimal rule sets in the knowledge integration phase. The experiments prove the rule sets derived by the proposed approach is more accurate than other machine learning algorithm.
A unifying ontology modeling for knowledge management
(2005)
Chen, AP
;
Chen, MY
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
The knowledge-based economy is approaching rapidly, and knowledge management (KM) has disseminated in leaps and bounds in academic circles as well as in the business world. This paper develops a unifying framework to evaluate KM activities for supporting intelligent knowledge-based system (KBS) using web interface, and expert system technology to help inexperienced administrators in insuring the smooth operation of KM performance. This paper makes three important contributions: (1) it proposes an efficient KM Ontology Construction Algorithm to fast conceptualize KM domain concept; (2) it provides a hybrid model used for knowledge acquisition through skeletal concept model and IDEF (Integrated DEFinition function modeling) analysis; and (3) it presents a methodology for using KM ontology in building a unifying framework and evaluation guideline for KM that works well and effective.
An association mining method for time series and its application in the stock prices of TFT-LCD industry
(2004)
Huang, CF
;
Chen, YC
;
Chen, AP
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
TFT-LCD is one of industries currently promoted by the "Two Trillion and Twin Star Industries Development Plan" in Taiwan. This study endeavors to find out the stock price associations between the suppliers and manufacturers in the value chain of the TFT-LCD industry by means of data mining techniques, and meanwhile, to improve the Apriori algorithm so that it can facilitate association mining of discrete data points in a time series. An efficient data mining method which consists of two phases is proposed. In the first phase, data are classified and preprocessed using the algorithm proposed by R. Agrawal et al. (1996), then Apriori algorithm is applied to extract the strong association rules. The second phase further improves the Apriori algorithm by breaking down the traditional limitation of relying on pattern matching of continuous data for disclosing stock market behavior. By mining the association rules from the discrete data points in a time series and testing the corresponding hypotheses, statistically significant outcomes can be obtained. The proposed data mining method was applied to some real time-series of the stock prices of companies in the supply chain of TFT-LCD industry in Taiwan. It is suggested that a positive correlation does not necessarily exist between the companies' stock prices in the supply chain of TFT-LCD industry. For instance the result shows that, if the stock price of Sintek Phonrotic Corp., a company in the up stream of the value chain, soars for more than 5% in a day, the stock price of Tatung, a company in the down stream of the same value chain, may not respond positively accordingly. If an investor can short the stock of Tatung on the 7th day and long it back on the 10th day after Sintek's stock price soaring for more than 5%, the annual return of investment is 199.88% with 95% confidence interval. In conclusion, the results may reveal helpful information for the investors to make leveraged arbitrage profit investing decisions, and it might be interesting to apply this proposed data mining method to the time series in other industries or problems and investigate the results further.
An extended two-phase architecture for mining time series data
(2005)
Chen, AP
;
Chen, YC
;
Hsu, NW
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Time series data vary with time. In the past, most of the researches focused on the matching of feature points or measuring of the similarities. They could successfully represent the feature patterns in a visualized way. In the mean while, those researches did not sufficiently describe the results in simple and understandable words. In this research, a two-phase architecture for mining time series data is introduced. By combining some different mining techniques, the difficulties mentioned above may be overcome. This architecture mainly consists of Exploratory Data Analysis (EDA) and techniques related to mining association rules. After the phase I analysis, quantitative association rules are obtained by phase II. Meanwhile, the rules of the architecture are able to be verified by accuracy analysis. Finally, a result of comparison with the traditional data mining techniques and this architecture shows that the two-phase architecture is superior to traditional techniques to the time series data.
An implementation of learning classifier systems for rule-based machine learning
(2005)
Chen, AP
;
Chen, MY
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Machine learning methods such as fuzzy logic, neural networks and decision tree induction have been applied to learn rules, however they can get trapped into a local optimal. Based on the principle of natural evolution and global searching, a genetic algorithm is promising for obtaining better results. This article adopts the learning classifier systems (LCS) technique to provide a hybrid knowledge integration strategy, which makes for continuous and instant teaming while integrating multiple rule sets into a centralized knowledge base. This paper makes three important contributions: (1) it provides a knowledge encoding methodology to represent various rule sets that are derived from different sources, and that are encoded as a fixed-length bit string; (2) it proposes a knowledge integration methodology to apply genetic operations and credit assignment to generate optimal rule sets; (3) it uses three criteria (accuracy, coverage, and fitness) to apply the knowledge extraction process, which is very effective in selecting an optimal set of rules from a large population. The experiments prove that the rule sets derived by the proposed approach is more accurate than the Fuzzy ID3 algorithm.
An intelligent model for stock investment with buffett strategy, classifier system, neural network and linear programming
(2004)
Chen, AP
;
Lin, WL
;
Chen, YC
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
"The Intelligent Model for Stock Investment with Buffett Strategy, Classifier System, Neural Network and Linear Programming" was studied for developing an intelligent model which call learn more knowledge regarding to stock investment with artificial intelligence technology. Classifier system, neural network, fundamental financial investment factors and linear programming are the fundamental components for the research. Knowledge transformation and Genetic evolution capability was discussed in the article, too. Furthermore, the investment strategy developed by Warren E. Buffett[17], the great financial investment master, was the major knowledge which was practiced in the article. For realizing more detail about learning system, a lot of topics regarding to artificial intelligence were discussed in advanced. including "A Market-Based Rule Learning System" [1], "Dynamic Trading Strategy Learning Model using Learning Classifier System" [2], "'Nonlinear Index Prediction" [3], "Financial Decision Support with Hybrid Genetic and Neural Based Modeling Tool" [4] and "Fuzzy Interval methods in Investment risk Appraisal" [5]. According to the Study mentioned above, the ideas to give intelligent model, especially with genetic algorithm, bring the direction for the advanced financial investment strategy and operation. Therefore, it was why a novel intelligent model with Buffett strategy, classifier system, neural network and linear programming proposed in the article.
Applying extending classifier system to develop an option-operation suggestion model of intraday trading - An example of Taiwan index option
(2005)
Chen, AP
;
Chen, YC
;
Tseng, WC
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
This novel study developed an option-operation suggestion model by applying integrated artificial intelligence technique, extending learning classifier system (XCS), which incorporates reinforcement machine learning method to the dynamical problems to the behavior finance. Due to the history of Behavior Finance, many researches have found that the shape of stock trend is not following random walk model, but the repeated trading patterns exist which are referred to as investors experiences. Furthermore, some classical researches have been merely adopted traditional artificial intelligence to analyze the result. Those methodologies are not sufficiently to resolve the dynamical problem, such as economical trading behaviors. Therefore, the model has been proposed concerning intraday trading but avoiding the system risk in the short-term position to benefit investors. By dynamic learning ability of XCS and general population features, the output operation suggestions could be obtained as a reference strategy for investors to predict the index option trend. As an example of Taiwan Index option, the results of the accuracy and accumulative profit have been exhibited remarkable outcome, and so as the simulations of short term prediction with 10-minute and 20-minute tick data.
Applying trie-structure to improve dynamic time warping on time-series stock data analysis
(2004)
Chen, AP
;
Chen, YC
;
Yeh, CY
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Dynamic time warping (DTW) is a robust but time-consuming on distance measure for time-series data similarity search. To speed up DTW for time-series data analysis, a new approach with trie-structure is introduced for the process of the DTW recognition steps, such as in financial stock pattern analysis. The final result shows that searching time has been reduced by this approach while applying trie-structure to DTW on time-series data analysis, especially on stock data.
Applying two-stage XCS model on global overnight effect local stock prediction
(2005)
Chen, AP
;
Chen, YC
;
Huang, YH
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
This study applied an integrated artificial intelligence method, extend learning classifier system (XCS), to predict the stock trend fluctuation considering the global overnight effect. However, some researchers have already indicated that XCS model that is applied successfully to form a forecast model in local market. Based on those prediction models, we put more effort to focus on the financial phenomenon, overnight effect between each two global markets, and we developed a two-stage XCS model to forecast the local stock market. In the experiments, DJi and Twi are chosen as referent and predicted markets respectively, and the model is trained by their historical data. For its accuracy verified, the model is tested by recently data. Finally, we have concluded that the proposed model successfully simulates the phenomenon, and the high ratio of correctness is definitely figured out.
Dynamic fuzzy OWA model for group multiple criteria decision making
(2006-05-01)
Chang, JR
;
Ho, TH
;
Cheng, CH
;
Chen, AP
;
資訊管理與財務金融系註：原資管所+財金所
Obtaining relative weights in MCDM problems is a very important issue. The Ordered Weighted Averaging (OWA) aggregation operators have been extensively adopted to assign the relative weights of numerous criteria. However, previous aggregation operators ( including OWA) are independent of aggregation situations. To solve the problem, this study proposes a new aggregation model-dynamic fuzzy OWA based on situation model, which can modify the associated dynamic weight based on the aggregation situation and can work like a "magnifying lens" to enlarge the most important attribute dependent on minimal information, or can obtain equal attribute weights based on maximal information. Two examples are adopted in this paper for comparison and showing the effects under different weights.
Evaluating the airline service quality by fuzzy OWA operators
(2005)
Cheng, CH
;
Chang, JR
;
Ho, TH
;
Chen, AP
;
資訊管理與財務金融系註：原資管所+財金所
The OWA (Ordered Weighted Averaging) aggregation operators have been extensively adopted to assign the relative weights of numerous criteria. However, previous aggregation operators (including OWA) are independent of aggregation situations. To solve the problem, this study proposes a new aggregation model - dynamic fuzzy OWA operators based on situation model, which can modify the associated dynamic weight based on the aggregation situation and can work like a "magnifying lens" to enlarge the most important attribute dependent on minimal information, or can obtain equal attribute weights based on maximal information. We also apply proposed model to evaluate the service quality of airline.
Fuzzy approaches for fault diagnosis of transformers
(2001-02-16)
Chen, AP
;
Lin, CC
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Dissolved gas analysis has been used as a diagnostic method to determine the conditions of transformers for a long time. The criteria used in dissolved gas analysis are based on crisp value norms. Due to the dichotomous nature of crisp criteria, transformers with similar gas-in-oil conditions may lead to very different conclusions of diagnosis especially when the gas concentrations are around the crisp norms. To deal with this problem, gas-in-oil data of failed transformers were collected and treated in order to obtain the membership functions of fault patterns using a fuzzy clustering method. All crisp norms are fuzzified to linguistic variables and diagnostic rules are transformed into fuzzy rules. A fuzzy system originally proposed by Takagi and Sugeno is used to combine the rules and the fuzzy conditions of transformers to obtain the final diagnostic results. It is shown that the diagnosing results from the combination of several simple fuzzy approaches are much better than traditional methods especially for transformers which have gas-in-oil conditions around the crisp norms. (C) 2001 Elsevier Science B.V. All rights reserved.
Fuzzy discriminant analysis with outlier detection by genetic algorithm
(2004-05-01)
Lin, CC
;
Chen, AP
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
This paper proposes a method for performing fuzzy multiple discriminant analysis on groups of crisp data and determining the membership function of each group by minimizing the classification error using a genetic algorithm. Euclidean distance is used to measure the similarity between data points and defining membership functions. A numerical example is provided for illustration. The numerical example indicates that the classification obtained by fuzzy discriminant analysis is more satisfactory than that obtained by crisp discriminant analysis and is less fuzzy than that obtained by fuzzy cluster analysis. Moreover, the proposed fuzzy discriminant analysis is also a good approach to identifying outliers, of which the degree of membership to each group is zero. (C) 2003 Elsevier Ltd. All rights reserved.
Generalization of Yang et al.'s method for fuzzy programming with piecewise linear membership functions
(2002-12-16)
Lin, CC
;
Chen, AP
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Li and Yu (Fuzzy Sets and Systems 101 (1999) 109) argued that the model of Yang et al. for fuzzy programming (Fuzzy Sets and Systems 41 (1991) 39) is correct only for a specific type of piecewise linear membership function and proposed their model for other type of membership functions. This study generalizes the model of Yang et al. A numerical example indicates that the model of Li and Yu is inapplicable to a fuzzy programming problem that involves more than one membership function, whereas the proposed model gives the optimal solution. (C) 2002 Elsevier Science B.V. All rights reserved.
Integrating extended classifier system and knowledge extraction model for financial investment prediction: An empirical study
(2006-07-01)
Chen, AP
;
Chen, MY
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Machine learning methods such as fuzzy logic, neural networks and decision tree induction have been applied to learn rules, however they can get trapped into a local optimal. Based on the principle of natural evolution and global searching, a genetic algorithm is promising for obtaining better results. This article adopts the learning classifier systems (LCS) technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multple rule sets into a centralized knowledge base. This paper makes three important contributions: (1) it represents various rule sets that are derived from different sources and encoded as a fixed-length bit string in the knowledge encoding phase; (2) it uses three criteria (accuracy, coverage, and fitness) to select an optimal set of rules from a large population in the knowledge extraction phase; (3) it applies genetic operations to generate optimal rule sets in the knowledge integration phase. The experiments prove that the rule sets derived by the proposed approach is more accurate than other machine learning algorithm. (c) 2005 Elsevier Ltd. All rights reserved.
Integrating option model and knowledge management performance measures: an empirical study
(2005)
Chen, MY
;
Chen, AP
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
The knowledge-based economy is coming, and knowledge management (KM) has rapidly disseminated in academic circles as well as in the business world. While an increasing number of companies have launched into knowledge management initiatives, a large proportion of these initiatives are limited to a technical focus. The problem with this type of focus is that it excludes and neglects the true potential benefits that can be derived from knowledge management. This paper develops a new metric, knowledge management performance index (KMPI), for evaluating the performance of a firm in its KM at a point in time. We therefore suggest that a KMPI can be used to determine KM activities from the following perspectives: knowledge creation, knowledge conversion, knowledge circulation and knowledge completion. When KM activities efficiency is increased, KMPI will also be expanded, enabling firms to become knowledge intensive. This paper makes three important contributions: (1) it provides a formal theoretical grounding for the validity of the Black-Scholes model that might be applied to KM; (2) it proposes a measurement framework to enable knowledge assets to be leveraged effectively and efficiently; and (3) it presents the first application of the Black-Scholes model that uses a real-world business situation involving KM as its test bed. The results prove the option pricing model can act as a measurement guideline to the whole range of KM activities.
Knowledge management performance evaluation: a decade review from 1995 to 2004
(2006)
Chen, MY
;
Chen, AP
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
In this paper, the development of knowledge management (KM) was surveyed, using a literature review and classification of articles from 1995 to 2004. With a keyword index and article abstract, we explored how KM performance evaluation has developed during this period. Based on a scope of 108 articles from 80 academic KM journals (retrieved from six online databases), we surveyed and classified methods of KM measurement, using the following eight categories: qualitative analysis, quantitative analysis, financial indicator analysis, non-financial indicator analysis, internal performance analysis, external performance analysis, project-orientated analysis and organization-orientated analysis, together with their measurement matrices for different research and problem domains. Future development directions for KM performance evaluation are presented in our discussion. They include: (1) KM performance measurements have tended towards expertise orientation, while evaluation development is a problem-orientated domain; (2) different information technology methodologies, such as expert systems, knowledge-based systems and case-based reasoning may be able to evaluate KM as simply another methodology; (3) the ability to continually change and obtain new understanding is the driving power behind KM methodologies, and should be the basis of KM performance evaluations in the future.
Measurement practices for knowledge management: An option perspective
(2005)
Chen, AP
;
Chen, MY
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
This article develops an option pricing model to evaluate knowledge management (KM) activities from the following perspectives: knowledge creation, knowledge conversion, knowledge circulation, and knowledge carry out. This paper makes three important contributions: (1) it provides a formal theoretical grounding for the validity of the Black-Scholes model that might be employed to KM; (2) it proposes a measurement framework to enable leveraging knowledge assets effectively and efficiently; (3) it presents the first application of the Black-Scholes model that uses a real world business situation involving KM as its test bed. The results prove the option pricing model can be act as a measurement guideline to the whole KM activities.
Reengineering supply chain management architecture - For the collaboration of marketing dynamics and manufacturing strategy
(2003)
Chen, AP
;
Liao, A
;
交大名義發表
;
National Chiao Tung University
Supply Chain Management (SCM) is the contemporary way to fulfill customer satisfaction and corporate objectives. It's brilliant at creating company value with the minimum cost to the world-class manufacture and service provider. However, from both the paper review and the observation of the global industry, it shows that the industry was dramatically suffered the Bullwhip Effect [1] in supply chains, illustrated as Figure 1. From 1983 to the present, this Bullwhip Effect was also affected in the semiconductor industry heavily and could be clearly illustrated by the dramatic fluctuation and deviation of world wide wafer fabrication equipment shipments and semiconductor shipments. [2].
Strategy of futures trading mechanism using extended classifier system
(2004)
Cheng, TW
;
Tsai, WC
;
Chen, AP
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Nowadays many artificial intelligent trading models divided the process in three separate subprocesses: trading, validation and application, but these models cannot meet the request of today's trading environment. A new online learning algorithm, extended classifier system (XCS) is used in futures extended classifier trading mechanism (FXCTM) to satisfy traders' requirement. This paper verifies that FXCTM provides a very good forecast ability in futures market trading performance. Also, this paper discusses about how the population set of XCS affects the result of the model. Finally, the simulation results show that this model could get an obvious profit from futures market.