This paper proposed a novel multi-objective affinity set (MO affinity set) classification system comparing with Ant colony optimization (ACO) and affinity set theory on delayed diagnosis dataset classification. The output of MO affinity set classification rules has the higher accuracy than ACO and traditional affinity set. Furthermore, our MO affinity set classification skips the traditional affinity set k-core method, and has fewer rules. It is better and more easily to apply or to construct a support system if the number of rules is smaller.
{"title":"A Novel Multi-objective Affinity Set Classification System: An Investigation of Delayed Diagnosis Detection","authors":"Chih H. Wu, Wei-Ting Li, Chin-Chia Hsu, Chi-Hua Li, I-Ching Fang, Chia-Hsiang Wu","doi":"10.1109/ACIIDS.2009.42","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.42","url":null,"abstract":"This paper proposed a novel multi-objective affinity set (MO affinity set) classification system comparing with Ant colony optimization (ACO) and affinity set theory on delayed diagnosis dataset classification. The output of MO affinity set classification rules has the higher accuracy than ACO and traditional affinity set. Furthermore, our MO affinity set classification skips the traditional affinity set k-core method, and has fewer rules. It is better and more easily to apply or to construct a support system if the number of rules is smaller.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130212578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A fuzzy classifier with adaptive learning of the volume of norm inducing matrix is proposed in this paper. The proposed fuzzy classifier improves the Gustafson-Kessel algorithm (GKA) which assumes a fixed volume of the norm inducing matrix. An efficient approach based on gradient descent learning, called adaptive ellipsoid classification algorithm (AECA) is proposed to recursively update the volume of norm inducing matrix. Mathematical analyses and computer simulations are made to show the effectiveness and efficiency of the proposed fuzzy classifier.
{"title":"A Fuzzy Classifier with Directed Initialization Adaptive Learning of Norm Inducing Matrix","authors":"L. Yao, Kuei-Sung Weng, Ren-Wei Chang","doi":"10.1109/ACIIDS.2009.57","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.57","url":null,"abstract":"A fuzzy classifier with adaptive learning of the volume of norm inducing matrix is proposed in this paper. The proposed fuzzy classifier improves the Gustafson-Kessel algorithm (GKA) which assumes a fixed volume of the norm inducing matrix. An efficient approach based on gradient descent learning, called adaptive ellipsoid classification algorithm (AECA) is proposed to recursively update the volume of norm inducing matrix. Mathematical analyses and computer simulations are made to show the effectiveness and efficiency of the proposed fuzzy classifier.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"841 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116375044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jonas C. P. Yu, Yu-Siang Lin, Kung-Jeng Wang, H. Wee
This paper develops a mathematical inventory model of deteriorating item taking into account a three-echelon supply chain vertical integration through strategic alliances. The objective of this model is to minimize the joint total relevant cost and to devise a compensation policy. Due to the complexity of the non-linear problems, it is not possible to find the global optimum analytically. Following the physical phenomenon of annealing, a soft computing method, Simulated Annealing (SA), has been developed to find the global optimum for the complex cost function through stochastic search process. A numerical example, sensitivity analysis, and the effects of the compensation policy on the optimal results are presented to validate the results of the proposed integrated model. The proposed mathematical model shows how an integrated approach to decision making can achieve a global optimum.
{"title":"Using AI Approach to Solve an Integrated Three-Echelon Supply Chain Model with Strategic Alliances","authors":"Jonas C. P. Yu, Yu-Siang Lin, Kung-Jeng Wang, H. Wee","doi":"10.1109/ACIIDS.2009.26","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.26","url":null,"abstract":"This paper develops a mathematical inventory model of deteriorating item taking into account a three-echelon supply chain vertical integration through strategic alliances. The objective of this model is to minimize the joint total relevant cost and to devise a compensation policy. Due to the complexity of the non-linear problems, it is not possible to find the global optimum analytically. Following the physical phenomenon of annealing, a soft computing method, Simulated Annealing (SA), has been developed to find the global optimum for the complex cost function through stochastic search process. A numerical example, sensitivity analysis, and the effects of the compensation policy on the optimal results are presented to validate the results of the proposed integrated model. The proposed mathematical model shows how an integrated approach to decision making can achieve a global optimum.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"41 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126004960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Kohonen Self Organizing Map (SOM) is an excellent tool in exploratory phase of data mining. The SOM is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units needs to be grouped i.e., clustered. In this paper a two-level clustering based on SOM is proposed, which employs rough set theory to capture the inherent uncertainty involved in cluster analysis. The two-stage procedure (first using SOM to produce the prototypes that are then clustered in the second stage) is found to perform well when compared with crisp clustering of the data and increase the accuracy.
{"title":"Rough Set Based Clustering of the Self Organizing Map","authors":"E. Mohebi, M. Sap","doi":"10.1109/ACIIDS.2009.79","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.79","url":null,"abstract":"The Kohonen Self Organizing Map (SOM) is an excellent tool in exploratory phase of data mining. The SOM is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units needs to be grouped i.e., clustered. In this paper a two-level clustering based on SOM is proposed, which employs rough set theory to capture the inherent uncertainty involved in cluster analysis. The two-stage procedure (first using SOM to produce the prototypes that are then clustered in the second stage) is found to perform well when compared with crisp clustering of the data and increase the accuracy.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126883641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In SCM, the problem with RFID data is that the volume increases according to time and location, thus, resulting in an enormous degree of data duplication. Therefore it is difficult to extract useful knowledge hidden in data using existing association rule mining techniques, or analyze data using statistical techniques or queries. However, strong associations discovered at high concept levels may represent common sense knowledge and RFID data represented as a concept hierarchy has the property that the data size at the lowest level increases in proportion to the item group. This paper has two aims. Firstly, we use time generalization to eliminate data duplication. Generalization is useful in data mining since they permit the discovery of knowledge at different levels of abstraction, such as multilevel association rules. Secondly, to reduce the complexity of rule generation by examining association rules limited to the level of interest of the consumer, not all concept hierarchy level on a each concept level have its own level passage threshold. As a result, rule generation time is reduced and the query speed is significantly accelerated, due to filtering of data.
{"title":"Mining Multilevel Association Rules on RFID Data","authors":"Younghee Kim, U. Kim","doi":"10.1109/ACIIDS.2009.32","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.32","url":null,"abstract":"In SCM, the problem with RFID data is that the volume increases according to time and location, thus, resulting in an enormous degree of data duplication. Therefore it is difficult to extract useful knowledge hidden in data using existing association rule mining techniques, or analyze data using statistical techniques or queries. However, strong associations discovered at high concept levels may represent common sense knowledge and RFID data represented as a concept hierarchy has the property that the data size at the lowest level increases in proportion to the item group. This paper has two aims. Firstly, we use time generalization to eliminate data duplication. Generalization is useful in data mining since they permit the discovery of knowledge at different levels of abstraction, such as multilevel association rules. Secondly, to reduce the complexity of rule generation by examining association rules limited to the level of interest of the consumer, not all concept hierarchy level on a each concept level have its own level passage threshold. As a result, rule generation time is reduced and the query speed is significantly accelerated, due to filtering of data.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127954711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, we propose a method of stability analysis for a GA-Based reference ANNC capable of handling these types of problems for a nonlinear system. The initial values of the consequent parameter vector are decided via a genetic algorithm (GA) after which a modified adaptive law is derived based on Lyapunov stability theory to control the nonlinear system for tracking a user-defined reference model. The requirement of Kalman-Yacubovich lemma is fulfilling. A boundary-layer function is introduced into these updating laws to cover parameter and modeling errors, and to guarantee that the state errors converge into a specified error bound. After this, an adaptive neural network controller (ANNC) is derived to simultaneously stabilize and control the system.
{"title":"Stability Analysis of Fuzzy Control for Nonlinear Systems","authors":"Po-Chen Chen, K. Yeh, Cheng-Wu Chen, Shu-Hao Lin","doi":"10.1109/ACIIDS.2009.83","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.83","url":null,"abstract":"In this study, we propose a method of stability analysis for a GA-Based reference ANNC capable of handling these types of problems for a nonlinear system. The initial values of the consequent parameter vector are decided via a genetic algorithm (GA) after which a modified adaptive law is derived based on Lyapunov stability theory to control the nonlinear system for tracking a user-defined reference model. The requirement of Kalman-Yacubovich lemma is fulfilling. A boundary-layer function is introduced into these updating laws to cover parameter and modeling errors, and to guarantee that the state errors converge into a specified error bound. After this, an adaptive neural network controller (ANNC) is derived to simultaneously stabilize and control the system.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132774439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, two concepts from different research areas are addressed together, namely functional dependency (FD) and multidimensional association rule (MAR). FD is a class of integrity constraints that have gained fundamental importance in relational database design. MAR is a class of patterns which has been studied rigorously in data mining. We employ MAR to mine the interesting rules from XML Databases. The mined interesting rules are considered as candidate FDs whose all confidence itemsets are 100%. To prune the weak rules, we pay attention to support and correlation itemsets. The final strong rules are used to generate an Object-Role Model conceptual schema diagram.
{"title":"Deriving Conceptual Schema from XML Databases","authors":"O. Y. Yuliana, S. Chittayasothorn","doi":"10.1109/ACIIDS.2009.13","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.13","url":null,"abstract":"In this paper, two concepts from different research areas are addressed together, namely functional dependency (FD) and multidimensional association rule (MAR). FD is a class of integrity constraints that have gained fundamental importance in relational database design. MAR is a class of patterns which has been studied rigorously in data mining. We employ MAR to mine the interesting rules from XML Databases. The mined interesting rules are considered as candidate FDs whose all confidence itemsets are 100%. To prune the weak rules, we pay attention to support and correlation itemsets. The final strong rules are used to generate an Object-Role Model conceptual schema diagram.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132261196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we proposed a novel house prediction model that integrated hybrid genetic-based support vector regression (HGA-SVR) model and Feng Shui theories for developing a high accuracy appraising real estate price system in Taiwan. In Taiwan, Feng Shui theory applies in choosing good days, divination and house selection. From the past researches, many factors might affect the real estate price which are the announced land values, the building room age, building total number of floor, the transportation condition and surrounding environment of house etc. However, few studies have been considered the Feng Shui effects in appraising real estate price. Therefore, the present study pioneers in applying Feng Shui theories for developing a high accuracy real estate price prediction system with back-propagation neural network(BPN), fuzzy neural network (FNN) and Hybrid Genetic-based SVR (HGA-SVR) to compare.Our results obtained from the comparison between two house price models with various artificial neural network models. By comparing the accuracy with various network architectures, the result demonstrates that HGA-SVR is the best network architecture and the Feng Shui model has a better performance in BPN, FNN and HGA-SVR. Our house price prediction system discovers some real estate price much higher than the reasonable prices. This result shows these unreasonable price needs adjusting to become more reasonable to conform the housing market.
{"title":"Hybrid Genetic-Based Support Vector Regression with Feng Shui Theory for Appraising Real Estate Price","authors":"Chih H. Wu, Chi-Hua Li, I-Ching Fang, Chin-Chia Hsu, Wei-Ting Lin, Chia-Hsiang Wu","doi":"10.1109/ACIIDS.2009.41","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.41","url":null,"abstract":"In this paper, we proposed a novel house prediction model that integrated hybrid genetic-based support vector regression (HGA-SVR) model and Feng Shui theories for developing a high accuracy appraising real estate price system in Taiwan. In Taiwan, Feng Shui theory applies in choosing good days, divination and house selection. From the past researches, many factors might affect the real estate price which are the announced land values, the building room age, building total number of floor, the transportation condition and surrounding environment of house etc. However, few studies have been considered the Feng Shui effects in appraising real estate price. Therefore, the present study pioneers in applying Feng Shui theories for developing a high accuracy real estate price prediction system with back-propagation neural network(BPN), fuzzy neural network (FNN) and Hybrid Genetic-based SVR (HGA-SVR) to compare.Our results obtained from the comparison between two house price models with various artificial neural network models. By comparing the accuracy with various network architectures, the result demonstrates that HGA-SVR is the best network architecture and the Feng Shui model has a better performance in BPN, FNN and HGA-SVR. Our house price prediction system discovers some real estate price much higher than the reasonable prices. This result shows these unreasonable price needs adjusting to become more reasonable to conform the housing market.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122715368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Chen, Kai-Fan Cheng, Ying-Hao Chen, Chia-Fen Hsieh
The main function of IDS (Intrusion Detection System) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a normal behavior. Though IDS has been developed for many years, the large number of return alert messages makes managers maintain system inefficiently. In this paper, we use RST (Rough Set Theory) and SVM (Support Vector Machine) to detect intrusions. First, RST is used to preprocess the data and reduce the dimensions. Next, the features selected by RST will be sent to SVM model to learn and test respectively. The method is effective to decrease the space density of data. The experiments will compare the results with different methods and show RST and SVM schema could improve the false positive rate and accuracy.
{"title":"Using Rough Set and Support Vector Machine for Network Intrusion Detection System","authors":"R. Chen, Kai-Fan Cheng, Ying-Hao Chen, Chia-Fen Hsieh","doi":"10.1109/ACIIDS.2009.59","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.59","url":null,"abstract":"The main function of IDS (Intrusion Detection System) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a normal behavior. Though IDS has been developed for many years, the large number of return alert messages makes managers maintain system inefficiently. In this paper, we use RST (Rough Set Theory) and SVM (Support Vector Machine) to detect intrusions. First, RST is used to preprocess the data and reduce the dimensions. Next, the features selected by RST will be sent to SVM model to learn and test respectively. The method is effective to decrease the space density of data. The experiments will compare the results with different methods and show RST and SVM schema could improve the false positive rate and accuracy.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123910978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research develops a weighted evolving fuzzy neural network for electricity demand forecasting in Taiwan. This study modifies the Evolving Fuzzy Neural Network Framework (EFuNN framework) and adopts a weighted factor to calculate the importance of each factor among these different rules. In addition, an exponential transfer function (exp(-D)) is employed to transfer the distance of any two factors into the value of similarity among different rules, thus a different rule clustering method is developed accordingly. Seven explanatory factors identified by the Taiwan Power Company will affect the power consumption in Taiwan and these seven factors will be inputted into the WEFuNN to forecast the electricity demand in the future. The historical data will be applied to train the WEFuNN and then forecasts the future electricity demands. Finally, the model is compared with other approaches proposed in the literature. The experimental results reveal that the MAPE for WEFuNN model is 6.11% which outperforms the others. In summary, the WEFuNN model can be applied practically as an electricity demand forecasted tool in Taiwan.
{"title":"A Weighted Evolving Fuzzy Neural Network for Electricity Demand Forecasting","authors":"P. Chang, C. Fan, J. Hsieh","doi":"10.1109/ACIIDS.2009.93","DOIUrl":"https://doi.org/10.1109/ACIIDS.2009.93","url":null,"abstract":"This research develops a weighted evolving fuzzy neural network for electricity demand forecasting in Taiwan. This study modifies the Evolving Fuzzy Neural Network Framework (EFuNN framework) and adopts a weighted factor to calculate the importance of each factor among these different rules. In addition, an exponential transfer function (exp(-D)) is employed to transfer the distance of any two factors into the value of similarity among different rules, thus a different rule clustering method is developed accordingly. Seven explanatory factors identified by the Taiwan Power Company will affect the power consumption in Taiwan and these seven factors will be inputted into the WEFuNN to forecast the electricity demand in the future. The historical data will be applied to train the WEFuNN and then forecasts the future electricity demands. Finally, the model is compared with other approaches proposed in the literature. The experimental results reveal that the MAPE for WEFuNN model is 6.11% which outperforms the others. In summary, the WEFuNN model can be applied practically as an electricity demand forecasted tool in Taiwan.","PeriodicalId":275776,"journal":{"name":"2009 First Asian Conference on Intelligent Information and Database Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127803113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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