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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3282 条记录，以下是2931-2940 订阅

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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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The ICSI-SRI spring 2006 meeting recognition system

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3rd international Workshop on machine learning for Multimodal Interaction, MLMI 2006

作者： Janin, Adam Stolcke, Andreas Anguera, Xavier Boakye, Kofi Çetin, Özgür Frankel, Joe Zheng, Jing International Computer Science Institute Berkeley CA United States SRI International Menlo Park CA United States Technical University of Catalonia Barcelona Spain

ISBN: (纸本)3540692673

We describe the development of the ICSI-SRI speech recognition system for the National Institute of Standards and Technology (NIST) Spring 2006 Meeting Rich Transcription (RT-06S) evaluation, highlighting improvements made since last year, including improvements to the delay-and-sum algorithm, the nearfield segmenter, language models, posterior-based features, HMM adaptation methods, and adapting to a small amount of new lecture data. Results are reported on RT-05S and RT-06S meeting data. Compared to the RT-05S conference system, we achieved an overall improvement of 4% relative in the MDM and SDM conditions, and 11% relative in the IHM condition. On lecture data, we achieved an overall improvement of 8% relative in the SDM condition, 12% on MDM, 14% on ADM, and 15% on IHM. © 2006 Springer-Verlag Berlin/Heidelberg.

关键词： Speech recognition

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A clustering model for mining consumption patterns from imprecise electric load time series data

引用

3rd international conference on Fuzzy Systems and Knowledge Discovery

作者： Li, Qiudan Liao, Stephen Shaoyi Li, Dandan Chinese Acad Sci Inst Automat Lab Complex Syst & Intelligence Sci Beijing Peoples R China City Univ Hong Kong Sch Econ & Management Dept Informat Syst Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Automat & Comp Aided Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)3540459162

This paper presents a novel clustering model for mining patterns from imprecise electric load time series. The model consists of three components. First, it contains a process that deals with representation and preprocessing of imprecise load time series. Second, it adopts a similarity metric that uses interval semantic separation (Interval SS)-based measurement. Third, it applies the similarity metric together with the k-means clustering method to construct clusters. The model gives a unified way to solve imprecise time series clustering problem and it is applied in a real world application, to find similar consumption patterns in the electricity industry. Experimental results have demonstrated the applicability and correctness of the proposed model.

关键词： pattern recognition

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Using association rule mining for the QSAR problem

Using association rule mining for the QSAR problem

引用

international IEEE conference on Intelligent Systems, IS

作者： L. Dumitriu M-V. Craciun C. Segal A. Cocu L.P. Georgescu Computer Science Department Dunãrea de Jos Galati University Galati Romania Chemistry Department Dunãrea de Jos Galati University Galati Romania

There are several approaches in trying to solve the quantitative structure-activity (QSAR) problem. These approaches are based either on statistical methods or on predictive data mining. Among the statistical methods, one should consider regression analysis, pattern recognition (such as cluster analysis, factor analysis and principal components analysis) or partial least squares. Predictive data mining techniques use either neural networks, or genetic programming, or neuro-fuzzy knowledge. These approaches have a low explanatory capability or non at all. This paper attempts to establish a new approach in solving QSAR problems using descriptive data mining. This way, the relationship between the chemical properties and the activity of a substance would be comprehensibly modeled

关键词： Association rules data mining Statistical analysis pattern analysis Regression analysis pattern recognition Principal component analysis Least squares methods Neural networks Genetic programming

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An Automatic Method to Extract data from an Electronic Contract Composed of a Number of Documents in PDF Format

An Automatic Method to Extract Data from an Electronic Contr...

引用

IEEE international conference on e-Commerce Technology (CEC)

作者： T. Kwok Thao Nguyen IBM Research Division IBM Thomas J. Watson Research Center Hawthorne NY USA

An electronic contract can encompass a large number of collateral contract documents in PDF format. These contract documents are of different contract document types and converted from different original formats. data extraction and thus data mining for this kind of electronic contracts is very difficult. In this paper, we present a novel method to automatically extract contract data from this kind of electronic contracts. Our automatic electronic contract data extraction system comprises an administrator module, a PDF parser, a pattern recognition engine and a contract data extraction engine. The administrator module provides templates for inputting document patterns and a list of contract data tags for each contract document type. It also constructs the pattern matrices and stores them in a database. The PDF parser converts the contract PDF document into the contract text document with the insertion of formatting bookmarks, such as a new page, paragraph or line. The pattern recognition engine determines a list of contract document types in the electronic contract by comparing and matching the patterns of all known contract document types with the pattern of the contract text document. The contract data extraction engine retrieves the corresponding list of contract data tags and then extracts contract data accordingly for each contract document type on the list. Our automatic electronic contract data extraction system has found to be very accurate, efficient and useful in extracting contract data for data mining

关键词： data mining Contracts Search engines pattern recognition Matrix converters databases pattern matching Information retrieval Business Companies

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A Statistical Approach to Anomaly Detection in Interdomain Routing

A Statistical Approach to Anomaly Detection in Interdomain R...

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international conference on Broadband Networks (BroadNets)

作者： S. Deshpande M. Thottan T. K. Ho B. Sikdar Rensselaer Polytechnic Institute Troy NY USA Bell Laboratories Murray Hill NJ USA

A number of events such as hurricanes, earthquakes, power outages can cause large-scale failures in the Internet. These in turn cause anomalies in the interdomain routing process. The policy-based nature of border gateway protocol (BGP) further aggravates the effect of these anomalies causing severe, long lasting route fluctuations. In this work we propose an architecture for anomaly detection that can be implemented on individual routers. We use statistical pattern recognition techniques for extracting meaningful features from the BGP update message data. A time-series segmentation algorithm is then carried out on the feature traces to detect the onset of an instability event The performance of the proposed algorithm is evaluated using real Internet trace data. We show that instabilities triggered by events like router mis-configurations, infrastructure failures and worm attacks can be detected with a false alarm rate as low as 0.0083 alarms per hour. We also show that our learning based mechanism is highly robust as compared to methods like exponentially weighted moving average (EWMA) based detection.

关键词： Routing Internet Event detection Hurricanes Earthquakes Large-scale systems Protocols Fluctuations pattern recognition data mining

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Scalable Representative Instance Selection and Ranking

Scalable Representative Instance Selection and Ranking

引用

international conference on pattern recognition

作者： Xingquan Zhu Xindong Wu Department of Computer Science University of Vermont VT USA

ISBN: (纸本)0769525210

Finding a small set of representative instances for large datasets can bring various benefits to data mining practitioners so they can (1) build a learner superior to the one constructed from the whole massive data; and (2) avoid working on the whole original dataset all the time. We propose in this paper a scalable representative instance selection and ranking (SRISTAR pronounced 3STAR) mechanism, which carries two unique features: (1) it provides a representative instance ranking list, so that users can always select instances from the top to the bottom, based on the number of examples they prefer; and (2) it investigates the behaviors of the underlying examples for instance selection, and the selection procedure tries to optimize the expected future error. Given a dataset, we first cluster instances into small data cells, each of which consists of instances with similar behaviors. Then we progressively evaluate data cells and their combinations, and order them into a list such that the learners built from the top cells are more accurate

关键词： data mining Sampling methods data privacy Scalability Computer science machine learning algorithms Computational complexity machine learning Bagging Boosting

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Naive Bayes classifier: True and estimated errors for 2-class, 2-features case

Naive Bayes classifier: True and estimated errors for 2-clas...

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international IEEE conference on Intelligent Systems, IS

作者： Zoe Hoare School of Informatics University of Wales Gwynedd UK

The low error rate of naive Bayes (NB) classifier has been described as surprising. It is known that class conditional independence of the features is sufficient but not a necessary condition for optimality of NB. This study is about the difference between the estimated error and the true error of NB taking into account feature dependencies. Analytical results are derived for two binary features. Illustration examples are also provided

关键词： Niobium Diseases Error analysis Probability Intelligent systems machine learning pattern recognition Cows Humans Capacitive sensors

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Study on Knowledge Expression and Efficient Attribute Reduction Algorithm Based on Information Granule

Study on Knowledge Expression and Efficient Attribute Reduct...

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international IEEE conference on Intelligent Systems, IS

作者： Chen Xi Fu Ming Wang Xiaoqian School of Computer & Communication Engineering Changsha University of Science and Technology Changsha China

data mining refers to extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases. Noisy and inconsistent data are commonplace properties of large database and data warehouses. It is difficult when noisy and inconsistent data are mined by using classical rough set theory. In this paper, the concept of information granule is introduced. Then the knowledge possessing given confidence is described by using concept of information granule and the roughness and simpleness of knowledge is discussed by using extension of rough set theory. At last, the algorithm for attribute reduction based on information granule is presented. Experimental results show that the presented algorithm is good at enormous data production and effective to extract simplicity knowledge from noisy and inconsistent data with minimum confidence threshold

关键词： data mining Set theory Information systems Telephony Intelligent systems Deductive databases data warehouses Production learning systems machine learning

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Testing Attribute Selection Algorithms for Classification Performance on Real data

Testing Attribute Selection Algorithms for Classification Pe...

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international IEEE conference on Intelligent Systems, IS

作者： Mihaescu Marian Cristian Burdescu Dumitru Dan Software Engineering Department Faculty of Automation Computers and Electronics University of Cralova Romania

We have developed an e-learning platform which records all activity done by students. We try to create an analysis framework that may be used for proving whether or not the platform can classify the students according to their accumulated knowledge. In this process, attribute selection is one of the major initial steps that must be accomplished. In this paper we show what implications may have attribute selection for classification performance and which are the best techniques that may lead to concluding results

关键词： Classification algorithms Electronic learning Decision trees data engineering machine learning data mining Reliability engineering Materials testing Intelligent systems System testing

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