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Extended negative association rules and the corresponding mining algorithm

Extended negative association rules and the corresponding mi...

引用

4th international conference on machine learning and Cybernetics

作者： Gan, Min Zhang, Mingyi Wang, Shenwen Water Conservancy & Hdyropower Coll Dept Comp Hangzhou 310018 Zhejiang Peoples R China Guizhou Univ Sch Comp Sci & Engn Guiyang 550025 Guizhou Peoples R China Guizhou Acad Sci Guiyang 550001 Guizhou Peoples R China Guizhou Univ Sch Comp Sci & Engn Guiyang 550025 Guizhou Peoples R China

ISBN: (纸本)3540335846

Recently, negative association rule mining has received some attention and proved to be useful. this paper proposes an extended form for negative association rules and defines extended negative association rules. Furthermore, a corresponding algorithm is devised for mining extended negative association rules. the extended form is more general and expressive than the three existing forms. the proposed mining algorithm overcomes some limitations of previous mining methods, and experimental results show that it is efficient on simple and sparse datasets when minimum support is high to some degree. Our work will extend related applications of negative association rules to a broader range.

关键词： data mining

来源：评论

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the compound effect of boosting and stratified sampling on decision tree accuracy

The compound effect of boosting and stratified sampling on d...

引用

2nd international conference on Emerging Technologies

作者： Gill, Abdul Aziz Smith, George D. SZABIST Dept Comp Sci Islamabad Pakistan Univ E Anglia Sch Comp Sci Norwich NR4 7TJ Norfolk England

ISBN: (纸本)9781424405039

It is generally recognized that recursive partitioning, as used in the construction of class claaification trees, is inherently, unstable, particularly for small data sets. Classification accuracy and, by implication, tree structure, are sensitive to changes in the training data. Successful approaches to counteract this effect include multiple classifiers, e.g. boosting, bogging or windowing. the downside o(c) these multiple classification models, however, is the plethora of trees that result, often making it difficult to extract the classifier in a meaningful manner. We show that, by using some very weak knowledge in the sampling stage, when the data set is partitioned into the training and test sets, a more consistent and improved performance is achieved by a single decision tree classifier. the reductions in error rate attained are comparable with those attained using boosting. In addition, we demonstrate that the combination of such sampling, combined with boosting, yields significant reductions in error rates.

关键词： data mining pattern recognition machine learning

来源：评论

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data migration in RAID based on stripe unit heat

Data migration in RAID based on stripe unit heat

引用

4th international conference on machine learning and Cybernetics

作者： Liu, Yan Xie, Chang-Sheng Li, Huai-yang Zhao, Zhen Huazhong Univ Sci & Technol Key Lab Data Storage Syst Sch Comp Sci & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)3540335846

Traditional RAID has the characteristics that location of stripe unit in each disk is stochastic and static, and that the outer zone of the disk has higher data transfer rate as compared to the inner one. Facing this situation, to exploit RAID I/O performance fully, this paper proposes a new algorithm PMSH (Placement and Migration based on Stripe unit Heat) for RAID stripe unit data to be placed optically and migrated dynamically. Based on the heat of RAID stripe unit, PMSH keeps migrating the frequently accessed stripe unit to the disk zone with higher data transfer rate to optimize the location of data in RAID disks and make the data distribution adapt to the evolution of file access pattern dynamically as well. Simulation results demonstrate significant RAID I/O performance improvement using PMSH.

关键词： data transfer

来源：评论

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the way of adjusting parameters of the expert system shell McESE: New approach

The way of adjusting parameters of the expert system shell M...

引用

6th international Workshop on pattern recognition in Information Systems, PRIS 2006, in Conjunction with ICEIS 2006

作者： Bruha, I. Franek, F. McMaster University Department of Computing and Software Hamilton ON L8S4K1 Canada

ISBN: (纸本)9728865554

We have designed and developed a general knowledge representation tool, an expert system shell called McESE (McMaster Expert System Environment);it derives a set of production (decision) rules of a very general form. Such a production set can be equivalently symbolized as a decision tree. McESE exhibits several parameters such as the weights, thresholds, and the certainty propagation functions that have to be adjusted (designed) according to a given problem, for instance, by a given set of training examples. We can use the traditional machine learning (ML) or data mining (DM) algorithms for inducing the above parameters can be utilized. In this methodological case study, we discuss an application of genetic algorithms (GAs) to adjust (generate) parameters of the given tree that can be then used in the rule-based expert system shell McESE. the only requirement is that a set of McESE decision rules (or more precisely, the topology of a decision tree) be given.

关键词： Expert systems

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DRFE: Dynamic Recursive Feature Elimination for gene identification based on Random Forest

DRFE: Dynamic Recursive Feature Elimination for gene identif...

引用

13th international conference on Neural Informational Processing

作者： Nguyen, Ha-Nam Ohn, Syng-Yup Hankuk Aviat Univ Dept Comp Engn Seoul South Korea

ISBN: (纸本)3540464840

Determining the relevant features is a combinatorial task in various fields of machine learning such as text mining, bioinformatics, pattern recognition, etc. Several scholars have developed various methods to extract the relevant features but no method is really superior. Breiman proposed Random Forest to classify a pattern based on CART tree algorithm and his method turns out good results compared to other classifiers. Taking advantages of Random Forest and using wrapper approach which was first introduced by Kohavi et. al, we propose an algorithm named Dynamic Recursive Feature Elimination (DRFE) to find the optimal subset of features for reducing noise of the data and increasing the performance of classifiers. In our method, we use Random Forest as induced classifier and develop our own defined feature elimination function by adding extra terms to the feature scoring. We conducted experiments with two public datasets: Colon cancer and Leukemia cancer. the experimental results of the real world data showed that the proposed method has higher prediction rate compared to the baseline algorithm. the obtained results are comparable and sometimes have better performance than the widely used classification methods in the same literature of feature selection.

关键词： learning systems

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Missing or absent? A question in cost-sensitive decision tree 4

Missing or absent? A question in cost-sensitive decision tre...

引用

4th international conference on Active Media Technology

作者： Qin, Zhenxing Zhang, Shichao Zhang, Chengqi Univ Technol Sydney Fac Informat Technol POB 123 Sydney NSW 2007 Australia

ISBN: (纸本)9781586036157

One common source of error in data is the existence of missing value fields. Imputation method has been a widely used technique in preprocessing phase of data mining, in which missing values are replaced by some estimated values. Previous work is trying to seek the "original" values according to specific criteria, such as statistics measure. However, in domain of cost-sensitive leaning, minimal overall cost is the most important issue, i.e. a value which can minimize total cost is prefer than the "best" value upon common sense. For example, in medical domains, some data fields usually are left as absent and known information is enough for a decision. In this paper, we proposed a new method to study the problem of "missing or absent values?" in the domain cost-sensitive learning. Experiment results show some improvements with distinguished missing and absent data in cost-sensitive decision tree.

关键词： induction knowledge acquisition machine learning

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Feature selection for linear support vector machines

Feature selection for linear support vector machines

引用

international conference on pattern recognition

作者： Zhizheng Liang Tuo Zhao Biocomputing center Computing of Department Shenzhen Graduate School of Harbin Institute of Technoloy China

ISBN: (纸本)0769525210

Feature selection is attracted much interest from researchers in many fields such as pattern recognition and data mining. In this paper, a novel algorithm for feature selection is developed. the proposed algorithm uses the standard linear SVM algorithm and is performed in an iterative way. Feature selection is carried out by assigning weights to features. Experimental results on UCI data set and face images confirm the feasibility and validation of the proposed method

关键词： Support vector machines Iterative algorithms Support vector machine classification pattern recognition data mining machine learning Feature extraction Kernel Equations Statistics

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Improving Dynamic learning Vector Quantization

Improving Dynamic Learning Vector Quantization

引用

international conference on pattern recognition

作者： C. De Stefano C. D'Elia A. Marcelli A. Scotto di Freca DAEIIMI Universita' di Cassino Italy DIIIE Universita' di Salerno Italy DIIIE-Universita di Salerno Italy

We introduce some improvements to the dynamic learning vector quantization algorithm proposed by us for tackling the two major problems of those networks, namely neuron over-splitting and their distribution in the feature space. We suggest to explicitly estimate the potential improvement on the recognition rate achievable by splitting neurons in those regions of the feature space in which two or more classes overlap. We also suggest to compute the neuron splitting frequency, and to combine these information for selecting the most promising neuron to split. Experimental results on both synthetic and real data extracted from UCI machine learning Repository show substantial improvements of the proposed algorithm with respect to the state of the art

关键词： Vector quantization Neurons Clustering algorithms Power capacitors Frequency pattern recognition data mining machine learning machine learning algorithms Supervised learning

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Analyzing gene expression profiles with ICA

Analyzing gene expression profiles with ICA

引用

4th IASTED international conference on Biomedical Engineering

作者： Lutter, D. Stadlthanner, K. theis, F. Lang, E.W. Tomé, A.M. Becker, B. Vogt, th. Institute of Biophysics University of Regensburg 93040 Regensburg Germany DET/IEETA University of Aveiro 3810-193 Aveiro Clinic of Dermatology University Hospital Regensburg 93053 Regensburg Germany

ISBN: (纸本)0889865787

High-throughput genome-wide measurements of gene transcript levels have become available with the recent development of microarray technology. Intelligent and efficient mathematical and computational analysis tools are needed to read and interpret the information content buried in those large scale gene expression patterns at various levels of resolution. But the development of such methods is still in its infancy. Modern machine learning and data mining techniques based on information theory, like independent component analysis (ICA), consider gene expression patterns as a superposition of independent expression modes which are considered putative independent biological processes. We focus on two widely used ICA algorithms to blindly decompose gene expression profiles into independent component profiles representing underlying biological processes. these exploratory methods will be capable of detecting similarity, locally or globally, in gene expression patterns and help to group genes into functional categories - for example, genes that are expressed to a greater or lesser extent in response to a drug or an existing disease.

关键词： Gene transfer

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Robust Multiclass Ensemble Classifiers via Symmetric Functions

Robust Multiclass Ensemble Classifiers via Symmetric Functio...

引用

international conference on pattern recognition

作者： P. Lefaucheur R. Nock DSI Université Antilles-Guyane Martinique France

We introduce a generalization to the multiclass framework of a previous approach to boosting by constructing symmetric functions. this approach contrasts with the usual AdaBoost-type boosting algorithms using linear separators. Indeed, multiclass induction does not necessitate combination tricks such as those for linear separators, and it achieves some novel agnostic learning properties, as well as significant malicious noise tolerance. Experiments on a large testbed against AdaBoost and C4.5 display the efficiency of the approach proned

关键词： Robustness Boosting Particle separators machine learning algorithms Voting Testing Displays machine learning data mining Delta modulation

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