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39th IEEE Annual International Computer Software and Applications Conference Workshops (COMPSAC)

作者： Basu, Saikat DiBiano, Robert Karki, Manohar Stagg, Malcolm Weltman, Jerry Mukhopadhyay, Supratik Ganguly, Sangram Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA NASA Ames Res Ctr Bay Area Environm Res Inst Moffett Field CA 94035 USA

ISBN: (纸本)9781467365635

We present an agile framework for automated tracking of moving objects in full motion video (FMV). The framework is robust, being able to track multiple foreground objects of different types (e.g., person, vehicle) having disparate motion characteristics (like speed, uniformity) simultaneously in real time under changing lighting conditions, background, and disparate dynamics of the camera. It is able to start tracks automatically based on a confidence-based spatio-temporal filtering algorithm and is able to follow objects through occlusions. Unlike existing tracking algorithms, with high likelihood, it does not lose or switch tracks while following multiple similar closely-spaced objects. The framework is based on an ensemble of tracking algorithms that are switched automatically for optimal performance based on a performance measure without losing state. Only one of the algorithms, that has the best performance in a particular state is active at any time providing computational advantages over existing ensemble frameworks like boosting. A C++ implementation of the framework has outperformed existing visual tracking algorithms on most videos in the Video Image Retrieval and Analysis Tool (VIRAT: ***) and the Tracking-Learning-Detection data-sets.

关键词： Full Motion Video object tracking Confidence-based spatio-temporal filtering Agile tracking ensemble algorithm

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Tree-based ensemble methods and their applications in analytical chemistry

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TRAC-TRENDS IN ANALYTICAL CHEMISTRY 2012年 40卷 158-167页

作者： Cao, Dong-Sheng Xu, Qing-Song Zhang, Liang-Xiao Huang, Jian-Hua Liang, Yi-Zeng Cent South Univ Res Ctr Modernizat Tradit Chinese Med Changsha 410083 Peoples R China Cent South Univ Sch Math & Stat Changsha 410083 Peoples R China Chinese Acad Sci Dalian Inst Chem Phys Key Lab Separat Sci Analyt Chem Dalian 116023 Peoples R China

Large amounts of data from high-throughput analytical instruments have generally become more and more complex, bringing a number of challenges to statistical modeling. To understand complex data further, new statistically-efficient approaches are urgently needed to: (1) select salient features from the data;(2) discard uninformative data;(3) detect outlying samples in data;(4) visualize existing patterns of the data;(5) improve the prediction accuracy of the data;and, finally, (6) feed back to the analyst understandable summaries of information from the data. We review current developments in tree-based ensemble methods to mine effectively the knowledge hidden in chemical and biology data. We report on applications of these algorithms to variable selection, outlier detection, supervised pattern analysis, cluster analysis, and tree-based kernel and ensemble learning. Through this report, we wish to inspire chemists to take greater interest in decision trees and to obtain greater benefits from using the tree-based ensemble techniques. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Chemometrics Classification and regression tree (CART) Cluster analysis Complex data ensemble algorithm Kernel method Outlier detection Pattern analysis Tree-based ensemble Variable selection

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Crude oil price analysis and forecasting using wavelet decomposed ensemble model

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ENERGY 2012年第1期46卷 564-574页

作者： He, Kaijian Yu, Lean Lai, Kin Keung Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China City Univ Hong Kong Dept Management Sci Kowloon Hong Kong Peoples R China

To improve the forecasting accuracy of crude oil price with deeper understanding of the market microstructure, this paper proposes a wavelet decomposed ensemble model. The proposed model follows the Heterogeneous Market Hypothesis that assumes the unstationarity and dynamic changing nature of the underlying market structure and introduces the wavelet analysis to analyze the dynamic underlying Data Generating Process at finer time scale domain. The simple averaging based ensemble model is introduced to reduce the estimation bias resulting from the use of different wavelet families by deriving market consensus view. The ensemble members are selected dynamically based on their in-sample performance among forecast matrices based on different wavelet families. Results from empirical studies show the superior performance of the proposed algorithm against the benchmark models, in terms of both level and directional predictive accuracy. The proposed model can effectively extract and model the time varying heterogeneous market microstructure, whose accurate characterization results in further improvement in market analysis and predictability. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Wavelet analysis Crude oil price forecasting ensemble algorithm ARMA model Multiresolution analysis

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A New Fuzzy Classifier for Data Streams

A New Fuzzy Classifier for Data Streams

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11th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Pietruczuk, Lena Duda, Piotr Jaworski, Maciej Czestochowa Tech Univ Dept Comp Engn PL-42200 Czestochowa Poland

ISBN: (纸本)9783642293467;9783642293474

Along with technological developments we observe an increasing amount of stored and processed data. It is not possible to store all incoming data and analyze it on the fly. Therefore many researchers are working on new algorithms for data stream mining. New algorithm should be fast and should use a small amount of memory. We will consider the problem of data stream classification. To increase the accuracy we propose to use an ensemble of classifiers based on a modified FID3 algorithm. The experimental results show that this algorithm is fast and accurate. Therefore it is adequate tool for data stream classification.

关键词： classification ensemble algorithm FID3 data stream decision tree fuzzy logic

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Imputation of missing data using ensemble algorithms

Imputation of missing data using ensemble algorithms

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2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Lu, Xiaoling Si, Jiesheng Pan, Lanfeng Zhao, Yanyun Center for Applied Statistics Renmin University of China Beijing China School of Statistics Renmin University of China Beijing China

ISBN: (纸本)9781612841816

Missing data or incomplete data are very common in statistical situations. One way to deal with missing data is to conduct model imputation either one time or multiple times. One of the key problems in analyzing the imputed dataset is to give the valid statistical reference of the parameter estimated, that is, to give a right estimation of the standard error of the interested statistic. This paper proposes the new developed ensemble algorithms as imputation model. In order to realize multiple imputation, we suggest bootstrap sampling the prediction error several times. The properties of the proposed methods are studied by simulation and compared with existing methods. Finally, the methods are applied to analyze one real large dataset, taking the missing mechanism into consideration. © 2011 IEEE.

关键词： ensemble algorithm imputation missing data

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CLASSIFICATION OF LARGE MICROARRAY DATASETS USING FAST RANDOM FOREST CONSTRUCTION

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JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2011年第2期9卷 251-267页

作者： Manilich, Elena A. Oezsoyoglu, Z. Meral Trubachev, Valeriy Radivoyevitch, Tomas Case Western Reserve Univ Dept Comp Sci Cleveland OH 44106 USA Cleveland Clin Inst Digest Dis Cleveland OH 44195 USA Case Western Reserve Univ Dept Epidemiol & Biostat Cleveland OH 44106 USA

Random forest is an ensemble classification algorithm. It performs well when most predictive variables are noisy and can be used when the number of variables is much larger than the number of observations. The use of bootstrap samples and restricted subsets of attributes makes it more powerful than simple ensembles of trees. The main advantage of a random forest classifier is its explanatory power: it measures variable importance or impact of each factor on a predicted class label. These characteristics make the algorithm ideal for microarray data. It was shown to build models with high accuracy when tested on high-dimensional microarray datasets. Current implementations of random forest in the machine learning and statistics community, however, limit its usability for mining over large datasets, as they require that the entire dataset remains permanently in memory. We propose a new framework, an optimized implementation of a random forest classifier, which addresses specific properties of microarray data, takes computational complexity of a decision tree algorithm into consideration, and shows excellent computing performance while preserving predictive accuracy. The implementation is based on reducing overlapping computations and eliminating dependency on the size of main memory. The implementation's excellent computational performance makes the algorithm useful for interactive data analyses and data mining.

关键词： algorithm data mining genomic classifier random forest ensemble algorithm optimize bootstrap samples machine learning microarray analysis gene expression file-based implementation multi-dimensional data high-dimensional data

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An ensemble ELM Based on Modified *** algorithm for Predicting the Temperature of Molten Steel in Ladle Furnace

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2010年第1期7卷 73-80页

作者： Tian, Hui-Xin Mao, Zhi-Zhong Northeastern Univ Informat Sci & Engn Sch Shenyang Peoples R China Tianjin Polytech Univ Elect & Automat Engn Sch Tianjin Peoples R China

Combined the modified *** with extreme learning machine (ELM), a new hybrid artificial intelligent technique called ensemble ELM is developed for regression problem in this study. First, a new ELM algorithm is selected as ensemble predictor due to its rapid speed and good performance. Second, a modified *** is proposed to overcome the limitation of original *** by self-adaptively modifying the threshold value. Then, an ensemble ELM is presented by using the modified *** for better accuracy of predictability than individual method. Finally, this new hybrid intelligence method is used to establish a temperature prediction model of molten steel by analyzing the metallurgic process of ladle furnace (LF). The model is examined by data of production from 300t LF in Baoshan Iron and Steel Co., Ltd. and compared with the models that established by single ELM, GA-BP (combined genetic algorithm with BP network), and original ***. The experiments demonstrated that the hybrid intelligence method can improved generalization performance and boost the accuracy, and the accuracy of the temperature prediction is satisfied for the process of practical producing. Note to Practitioners-In practical industrial process, there are many important parameters that cannot be calculated accurately. Many intelligent methods have been used to estimate these parameters based on production data in the past decades. However, sometimes, the accuracy of estimation is not satisfied for industrial production. In this study, a new ensemble ELM is proposed using modified *** as a efficiently intelligent method. It is also used to establish an intelligent model for predicting the temperature of molten steel in LF as an example, and the good performance of the new ensemble is demonstrated. The new proposed method can boost the accuracy of the method that using single intelligent algorithm. Therefore, it can be widely used in intelligent prediction or estimati

关键词： AdaBoost.RT extreme learning machine (ELM) ensemble algorithm ladle furnace self-adaptive

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Training the genre classifier for automatic classification of web pages

Training the genre classifier for automatic classification o...

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29th International Conference on Information Technology Interfaces

作者： Vidulin, Vedrana Lustrek, Mitja Gams, Matjaz Jozef Stefan Inst Jamova 39 SI-1000 Ljubljana Slovenia

ISBN: (纸本)9789537138097

This paper presents experiments on classifying web pages by genre. Firstly, a corpus of 1539 manually labeled web pages was prepared. Secondly, 502 genre features were selected based on the literature and the observation of the corpus. Thirdly, these features were extracted from the corpus to obtain a data set. Finally, two machine learning algorithms, one for induction of decision trees (J48) and one ensemble algorithm (bagging), were trained and tested on the data set. The ensemble algorithm achieved on average 17% better precision and 1.6% better accuracy, but slightly worse recall;F-measure did not vary significantly. The results indicate that classification by genre could be a useful addition to search engines.

关键词： genre classification web page genre features ensemble algorithm

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