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检索条件"任意字段=5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004"

共 182 条记录，以下是171-180 订阅

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Exploiting safety constraints in fuzzy self-organising maps for safety critical applications

引用

5th international conference on intelligent data engineering and automated learning, ideal 2004

作者： Kurd, Zeshan Kelly, Tim P. Austin, Jim High Integrity Systems Engineering Group Department of Computer Science University of York YorkYO10 5DD United Kingdom Advanced Computer Architectures Group Department of Computer Science University of York YorkYO10 5DD United Kingdom

ISBN: (纸本)3540228810

this paper defines a constrained Artificial Neural Network (ANN) that can be employed for highly-dependable roles in safety critical applications. the derived model is based upon the Fuzzy Self-Organising Map (FSOM) and enables behaviour to be described qualitatively and quantitatively. By harnessing these desirable features, behaviour is bounded through incorporation of safety constraints – derived from safety requirements and hazard analysis. the constrained FSOM has been termed a ‘Safety Critical Artificial Neural Network’ (SCANN) and preserves valuable performance characteristics for nonlinear function approximation problems. the SCANN enables construction of compelling (product-based) safety arguments for mitigation and control of identified failure modes. Illustrations of potential benefits for real-world applications are also presented. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Safety factor

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Mining association rules using relative confidence

引用

5th international conference on intelligent data engineering and automated learning, ideal 2004

作者： Do, Tien Dung Hui, Siu Cheung Fong, Alvis C.M. School of Computer Engineering Nanyang Technological University Nanyang Avenue Singapore

ISBN: (纸本)3540228810

Mining for association rules is one of the fundamental tasks of data mining. Association rule mining searches for interesting relationships amongst items for a given dataset based mainly on the support and confidence measures. Support is used for filtering out infrequent rules, while confidence measures the implication relationships from a set of items to one another. However, one of the main drawbacks of the confidence measure is that it presents the absolute value of implication that does not reflect truthfully the relationships amongst items. For example, if two items have a very high frequency, then they will probably form a rule with a high confidence even if there is no relationship between them at all. In this paper, we propose a new measure known as relative confidence for mining association rules, which is able to reflect truthfully the relationships of items. the effectiveness of the relative confidence measure is evaluated in comparison with the confidence measure in mining interesting relationships between terms from textual documents and in associative classification. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Association rules

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Detecting credit card fraud by using questionnaire-responded transaction model based on support vector machines

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5th international conference on intelligent data engineering and automated learning, ideal 2004

作者： Chen, Rong-Chang Chiu, Ming-Li Huang, Ya-Li Chen, Lin-Ti Department of Logistics Engineering and Management China Department of Information Management National Taichung Institute of Technology No. 129 Sec. 3 Sanmin Rd Taichung404 Taiwan

ISBN: (纸本)3540228810

this work proposes a new method to solve the credit card fraud problem. Traditionally, systems based on previous transaction data were set up to predict a new transaction. this approach provides a good solution in some situations. However, there are still many problems waiting to be solved, such as skewed data distribution, too many overlapped data, fickle-minded consumer behavior, and so on. To improve the above problems, we propose to develop a personalized system, which can prevent fraud from the initial use of credit cards. First, the questionnaireresponded transaction (QRT) data of users are collected by using an online questionnaire based on consumer behavior surveys. the data are then trained by using the support vector machines (SVMs) whereby the QRT models are developed. the QRT models are used to predict a new transaction. Results from this study show that the proposed method can effectively detect the credit card fraud. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Consumer behavior

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learning users’ interests in a market-based recommender system

引用

5th international conference on intelligent data engineering and automated learning, ideal 2004

作者： Wei, Yan Zheng Moreau, Luc Jennings, Nicholas R. Intelligence Agents Multimedia Group School of Electronics and Computer Science University of Southampton United Kingdom

ISBN: (纸本)3540228810

Recommender systems are widely used to cope with the problem of information overload and, consequently, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. Our marketplace thus coordinates multiple recommender agents and ensures only the best recommendations are presented. To do this effectively, however, each agent needs to learn the users’ interests and adapt its recommending behaviour accordingly. To this end, in this paper, we develop a reinforcement learning and Boltzmann exploration strategy that the recommender agents can use for these tasks. We then demonstrate that this strategy helps the agents to effectively obtain information about the users’ interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Recommender systems

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Clustering model selection for reduced support vector machines

引用

5th international conference on intelligent data engineering and automated learning, ideal 2004

作者： Jen, Lih-Ren Lee, Yuh-Jye Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

ISBN: (纸本)3540228810

the reduced support vector machine was proposed for the practical objective that overcomes the computational difficulties as well as reduces the model complexity by generating a nonlinear separating surface for a massive dataset. It has been successfully applied to other kernel-based learning algorithms. Also, there are experimental studies on RSVM that showed the efficiency of RSVM. In this paper we propose a robust method to build the model of RSVM via RBF (Gaussian) kernel construction. Applying clustering algorithm to each class, we can generate cluster centroids of each class and use them to form the reduced set which is used in RSVM. We also estimate the approximate density for each cluster to get the parameter used in Gaussian kernel. Under the compatible classification performance on the test set, our method selects a smaller reduced set than the one via random selection scheme. Moreover, it determines the kernel parameter automatically and individually for each point in the reduced set while the RSVM used a common kernel parameter which is determined by a tuning procedure. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Radial basis function networks

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Prediction of implicit protein–protein interaction by optimal associative feature mining

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5th international conference on intelligent data engineering and automated learning, ideal 2004

作者： Eom, Jae-Hong Chang, Jeong-Ho Zhang, Byoung-Tak Biointelligence Lab School of Computer Science and Engineering Seoul National University Seoul151-744 Korea Republic of

ISBN: (纸本)3540228810

Proteins are known to perform a biological function by interacting with other proteins or compounds. Since protein–protein interaction is intrinsic to most cellular processes, protein interaction prediction is an important issue in post–genomic biology where abundant interaction data has been produced by many research groups. In this paper, we present an associative feature mining method to predict implicit protein–protein interactions of *** from public protein–protein interaction data. To overcome the dimensionality problem of conventional data mining approach, we employ feature dimension reduction filter (FDRF) method based on the information theory to select optimal informative features and to speed up the overall mining procedure. As a mining method to predict interaction, we use association rule discovery algorithm for associative feature and rule mining. Using the discovered associative feature we predict implicit protein interactions which have not been observed in training data. According to the experimental results, the proposed method accomplishes about 94.8% prediction accuracy with reduced computation time which is 32.5% faster than conventional method that has no feature filter. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Information theory

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Volatility forecasts in financial time series with HMM-GARCH models

引用

5th international conference on intelligent data engineering and automated learning, ideal 2004

作者： Zhuang, Xiong-Fei Chan, Lai-Wan Computer Science and Engineering Department The Chinese University of Hong Kong Shatin Hong Kong

ISBN: (纸本)3540228810

Nowadays many researchers use GARCH models to generate volatility forecasts. However, it is well known that volatility persistence, as indicated by the sum of the two parameters G1 and A1[1], in GARCH models is usually too high. Since volatility forecasts in GARCH models are based on these two parameters, this may lead to poor volatility forecasts. It has long been argued that this high persistence is due to the structure changes(e.g. shift of volatility levels) in the volatility processes, which GARCH models cannot capture. To solve this problem, we introduce our GARCH model based on Hidden Markov Models(HMMs), called HMM-GARCH model. By using the concept of hidden states, HMMs allow for periods with different volatility levels characterized by the hidden states. Within each state, local GARCH models can be applied to model conditional volatility. Empirical analysis demonstrates that our model takes care of the structure changes and hence yields better volatility forecasts. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Hidden Markov models

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Generating the reduced set by systematic sampling

引用

5th international conference on intelligent data engineering and automated learning, ideal 2004

作者： Chang, Chien-Chung Lee, Yuh-Jye Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

ISBN: (纸本)3540228810

the computational difficulties occurred when we use a conventional support vector machine with nonlinear kernels to deal with massive datasets. the reduced support vector machine (RSVM) replaces the fully dense square kernel matrix with a small rectangular kernel matrix which is used in the nonlinear SVM formulation to avoid the computational difficulties. In this paper, we propose a new algorithm, Systematic Sampling RSVM (SSRSVM) that selects the informative data points to form the reduced set while the RSVM used random selection scheme. this algorithm is inspired by the key idea of SVM, the SVM classifier can be represented by support vectors and the misclassified points are a part of support vectors. SSRSVM starts with an extremely small initial reduced set and adds a portion of misclassified points into the reduced set iteratively based on the current classifier until the validation set correctness is large enough. In our experiments, we tested SSRSVM on six public available datasets. It turns out that SSRSVM might automatically generate a smaller size of reduced set than the one by random sampling. Moreover, SSRSVM is faster than RSVM and much faster than conventional SVM under the same level of the test set correctness. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Support vector machines

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Clustering individuals in non-vector data and predicting: A novel model-based approach

Clustering individuals in non-vector data and predicting: A ...

引用

4th international conference on intelligent data engineering and automated learning (ideal 2003)

作者： Luo, KD Wang, JM Li, DY Sun, JG Tsing Hua Univ Sch Software Beijing 100084 Peoples R China

ISBN: (纸本)354040550X

In many applications, data is non-vector in nature. For example, one might have transaction data from a dialup access system, where each customer has an observed time-series of dialups which are different on start time and dialup duration from customer to customer. It's difficult to convert this type of data to a vector form, so that the existing algorithms oriented on vector data [5] are hard to cluster the customers with their dialup, events. this paper presents an efficient model-based algorithm to cluster individuals whose data is non-vector in nature. then we evaluate on a large data set of dialup transaction, in order to show that this algorithm is fast and scalable for clustering, and accurate for prediction. At the same time, we compare this algorithm with vector clustering algorithm by predicting accuracy, to show that the former is fitter for non-vector data than the latter.

关键词： Vectors

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Automating stock prediction with neural network and evolutionary computation

Automating stock prediction with neural network and evolutio...

引用

4th international conference on intelligent data engineering and automated learning, ideal 2003

作者： Ao, Sio Iong Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin Hong Kong

ISBN: (纸本)354040550X

In the previous studies [1, 2, 3], it has been found that there is strong correlation between the US market and the Asian markets in the long run. the VAR analysis shows that the US indices lead the Asian ones. But, such correlation is time-dependent and affects the performance of using the historical US data to predict the Asian markets by neural network. Here, a simplified automated system is outlined to overcome this difficulty by employing the evolutionary computation to simulate the markets interactive dynamics. the aim is to supplement the previous studies like [4, 5], which have focused more or less solely on the local stock market’s historical data, with additional information from other leading markets’ movements. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Automation

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