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Equivalence learning in protein classification 1

引用

5th international conference on machine learning and data mining in pattern recognition

作者： Kertesz-Farkas, Attila Kocsor, Andras Pongor, Sandor Hungarian Acad Sci Res Grp Artificial Intelligence Aradi Vertanuk Tere 1 H-6720 Szeged Hungary Technische Univ Dresden Erasmus Program Dresden Germany Appl Intelligence Lab Szeged Hungary Int Ctr Genetic Engn & Biotechnol Bioinformat Grp Trieste Italy Hungarian Acad Sci Biol Res Ctre Bioinformat Grp Szeged Hungary

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

We present a method, called equivalence learning, which applies a two-class classification approach to object-pairs defined within a multi-class scenario. the underlying idea is that instead of classifying objects into their respective classes, we classify object pairs either as equivalent (belonging to the same class) or non-equivalent (belonging to different classes). the method is based on a vectorisation of the similarity between the objects and the application of a machine learning algorithm (SVM, ANN, LogReg, Random Forests) to learn the differences between equivalent and non-equivalent object pairs, and define a, unique kernel function that can be obtained via equivalence learning. Using a small dataset of archaeal, bacterial and eukaryotic 3-phosphoglycerate-kinase sequences we found that the classification performance of equivalence learning slightly exceeds those of several simple machine learning algorithms at the price of a minimal increase in time and space requirements.

关键词： learning systems

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Extension classification method and its application based on extensible set

Extension classification method and its application based on...

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Yang, Chun-Yan Guangdong Univ Technol Res Inst Extens Engn Guangzhou 510090 Peoples R China

ISBN: (纸本)9781424410651

Based on the definitions of extensible set and the constructing method of its dependent function, a sort of classification method under extension tranformation, which is called extension classification method, is studied. It is different from the classification methods based on classical set, fuzzy set and rough set, and it is a sort of alterable classification method According to a certain transformation, it can divide a universe of discourse into 5 ports: positive extension field, negative extension field, positive stable field, negative stable field and extension boundary. Moreover, the universe of discourse and the dependent function describing the degree that an object possesses certain character are alterable. It makes the classification more elaborate. the phenomenon that "there is a corresponding classification pattern for a given transformation" is illuminated from the angle of set theory. Taking the extension classification management. on human resources as an example, its applied value will be explained. the classification method is a basic Method of extension data mining. It makes the classification function of data mining richer.

关键词： extensible set extension transformation classification extension classification data mining

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CCIC: Consistent common itemsets classifier 1

引用

5th international conference on machine learning and data mining in pattern recognition

作者： Shidara, Yohji Nakamura, Atsuyoshi Kudo, Mineichi Hokkaido Univ Grad Sch Informat Sci & Technol Kita Ku Kita 14Nishi 9 Sapporo Hokkaido 0600814 Japan

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

We propose a novel approach which extracts consistent (100% confident) rules and builds a classifier with them. Recently, associative classifiers which utilize association rules have been widely studied. Indeed, the associative classifiers often outperform the traditional classifiers. In this case, it is important to collect high quality (association) rules. Many algorithms find only high support rules, because decreasing the minimum support to be satisfied is computationally demanding. However, it may be effective to collect low support but high confidence rules. therefore, we propose an algorithm that produces a wide variety of 100% confident rules including low support rules. To achieve this goal, we adopt a specific-to-general rule searching strategy, in contrast to the previous many approaches. Our experimental results show that the proposed method achieves higher accuracies in several datasets taken from UCI machine learning repository.

关键词： data structures

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Fast-maneuvering target seeking based on double-action Q-learning 1

引用

5th international conference on machine learning and data mining in pattern recognition

作者： Ngai, Daniel C. K. Yung, Nelson H. C. Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

In this paper, a reinforcement learning method called DAQL is proposed to solve the problem of seeking and homing onto a fast maneuvering target, within the context of mobile robots. this Q-learning based method considers both target and obstacle actions when determining its own action decisions, which enables the agent to learn more effectively in a dynamically changing environment. It particularly suits fast-maneuvering target cases, in which maneuvers of the target are unknown a priori. Simulation result depicts that the proposed method is able to choose a less convoluted path to reach the target when compared to the ideal proportional navigation (IPN) method in handling fast maneuvering and randomly moving target. Furthermore, it can learn to adapt to the physical limitation of the system and do not require specific initial conditions to be satisfied for successful navigation towards the moving target.

关键词： moving object navigation reinforcement learning Q-learning

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PE-PUC: A graph based PU-learning approach for text classification 1

引用

5th international conference on machine learning and data mining in pattern recognition

作者： Yu, Shuang Li, Chunping Tsinghua Univ Sch Software Beijing 100084 Peoples R China

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

this paper presents a novel solution for the problem of building text classifier using positive documents (P) and unlabeled documents (U). Here, the unlabeled documents are mixed with positive and negative documents. this problem is also called PU-learning. the key feature of PU-learning is that there is no negative document for training. Recently, several approaches have been proposed for solving this problem. Most of them are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. Generally speaking, these existing approaches do not perform well when the size of P is small. In this paper, we propose a new approach aiming at improving the system when the size of P is small. this approach combines the graph-based semi-supervised learning method with the two-step method. Experiments indicate that our proposed method performs well especially when the size of P is small.

关键词： learning systems

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Comparison of a novel combined ECOC strategy with different multiclass algorithms together with parameter optimization methods 1

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5th international conference on machine learning and data mining in pattern recognition

作者： Hillsmann, Marco Friedrich, Christoph M. Univ Cologne D-5000 Cologne 41 Germany Fraunhofer Inst Algorithms & Sci Comp SCAI D-53754 Birlinghoven Germany

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

In this paper we consider multiclass learning tasks based on Support Vector machines (SVMs). In this regard, currently used methods are One-Against-All or One-Against-One, but there is much need for improvements in the field of multiclass learning. We developed a novel combination algorithm called Comb-ECOC, which is based on posterior class probabilities. It assigns, according to the Bayesian rule, the respective instance to the class with the highest posterior probability. A problem with the usage of a multiclass method is the proper choice of parameters. Many users only take the default parameters of the respective learning algorithms (e.g. the regularization parameter C and the kernel parameter gamma). We tested different parameter optimization methods on different learning algorithms and confirmed the better performance of One-Against-One versus One-Against-All, which can be explained by the maximum margin approach of SVMs.

关键词： learning algorithms

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Special periods peak load analysis and superior forecasting method based on LS-SVM

Special periods peak load analysis and superior forecasting ...

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Wang, Jian-Zhou Wu, Liang Lu, Hai-Yan Lanzhou Univ Sch Phys Sci & Technol Lanzhou 730000 Peoples R China Lanzhou Univ Sch Math & Stat Lanzhou 730000 Peoples R China Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9781424410651

People often try to smooth or eliminate load outliers all together in traditional power load forecasting. this, however, could result in the loss of important hidden information. In other words, the power load outliers themselves may be particular important. Hence there is a beforehand estimate to change and characteristic of power load, is a precondition of power system carry through economy dispatch, reduce production cost and prevent widespread blackout or collapse on electric system. In this paper propose a novel method for special periods power peak load detection, mining and forecasting. It incorporates the characteristic of high level load and maximum peak load analysis with optimum forecasting algorithm based on support vector machine. the validity of the method is proved by real data calculation.

关键词： load outliers data mining peak load forecast support vector machine

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Discovering relations among entities from XML documents 1

引用

5th international conference on machine learning and data mining in pattern recognition

作者： Wu, Yangyang Lei, Qing Luo, Wei Yokota, Harou Huaqiao Univ Dept Comp Sci Quanzhou Fujian Peoples R China Tokyo Inst Technol Dept Comp Sci Tokyo Japan

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

this paper addresses relation information extraction problem and proposes a method of discovering relations among entities which is buried in different nest structures of XML documents. the method first identifies and collects XML fragments that contain all types of entities given by users, then computes similarity between fragments based on semantics of their tags and their structures, and clusters fragments by similarity so that the fragments containing the same relation are clustered together, finally extracts relation instances and patterns of their occurrences from each cluster. the results of experiments show that the method can identify and extract relation information among given types of entities correctly from all kinds of XML documents with meaningful tags.

关键词： relation information extraction XML document cluster occurrence pattern

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Multi-agent system approach to react to sudden environmental changes 1

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5th international conference on machine learning and data mining in pattern recognition

作者： Raudys, Sarunas Mitasiunas, Antanas Inst Math & Informat Akad 4 LT-08663 Vilnius Lithuania Vilnius Univ Vilnius Lithuania

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Many processes experience abrupt changes in their dynamics. this causes problems for some prediction algorithms which assume that the dynamics of the sequence to be predicted are constant, or at least only change slowly over time. In this paper the problem of predicting sequences with sudden changes in dynamics is considered. For a model of multivariate Gaussian data we derive expected generalization error of standard linear Fisher classifier in situation where after unexpected task change, the classification algorithm learns on a mixture of old and new data. We show both analytically and by an experiment that optimal length of learning sequence depends on complexity of the task, input dimensionality, on the power and periodicity of. the changes. the proposed solution is to consider a collection of agents, in this case non-linear single layer perceptrons (agents), trained by a memetic like learning algorithm. T e most successful agents are voting for predictions. A grouped structure of the agent population assists in obtaining favorable diversity in the agent population. Efficiency of socially organized evolving multi-agent system is demonstrated on an artificial problem.

关键词： generalization dimensionality evolution learning multi-agent systems neural networks sample size

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Outlier detection with kernel density functions 1

引用

5th international conference on machine learning and data mining in pattern recognition

作者： Latecki, Longin Jan Lazarevic, Aleksandar Pokrajac, Dragojub Temple Univ CIS Dept Philadelphia PA 19122 USA United Technol Res Ctr E Hartford CT 06108 USA Delware State Univ CIS Dept CREOSA AMRC Dover DE 19901 USA

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foundation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Outliers are then detected by comparing the local density of each point to the local density of its neighbors. Our experiments performed on several simulated data sets have demonstrated that the proposed approach can outperform two widely used outlier detection algorithms (LOF and LOCI).

关键词： Financial data processing

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