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8th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

作者： Schaefer, Gerald Univ Loughborough Dept Comp Sci Loughborough Leics England

ISBN: (纸本)9781467364003;9781467363976

Pattern recognition problems occur in many fields and hence effective classification algorithms are the focus of much research. In various circumstances not classification accuracy but misclassification cost minimsation is the primary goal leading to the development of cost-sensitive classification algorithms. In this paper, we show how evolutionary algorithms, in particular genetic algorithms (GAs), can be employed optimise to cost-sensitive classifiers and classifier ensembles. In particular, we discuss how GAs can be employed to derive a compact set of fuzzy if-then rules with an embedded cost term, and how GAs are able to perform simultaneous classifier selection and fusion for ensemble classifiers.

关键词： Classifier Ensembles Classifiers Pattern recognition sorting algorithm evolutionary algorithm pattern recognition problem

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DRONE: Predicting Priority of Reported Bugs by Multi-Factor Analysis

DRONE: Predicting Priority of Reported Bugs by Multi-Factor ...

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29th IEEE International Conference on Software Maintenance (ICSM)

作者： Tian, Yuan Lo, David Sun, Chengnian Singapore Management Univ Singapore 178902 Singapore Natl Univ Singapore Singapore 117548 Singapore

Bugs are prevalent. To improve software quality, developers often allow users to report bugs that they found using a bug tracking system such as Bugzilla. Users would specify among other things, a description of the bug, the component that is affected by the bug, and the severity of the bug. Based on this information, bug triagers would then assign a priority level to the reported bug. As resources are limited, bug reports would be investigated based on their priority levels. This priority assignment process however is a manual one. Could we do better? In this paper, we propose an automated approach based on machine learning that would recommend a priority level based on information available in bug reports. Our approach considers multiple factors, temporal, textual, author, related-report, severity, and product, that potentially affect the priority level of a bug report. These factors are extracted as features which are then used to train a discriminative model via a new classification algorithm that handles ordinal class labels and imbalanced data. Experiments on more than a hundred thousands bug reports from Eclipse show that we can outperform baseline approaches in terms of average F-measure by a relative improvement of 58.61%.

关键词： bugs Bugs sorting algorithm precedence TRACKING SYSTEM

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Modeling Anomalies Prevalent In Sensor Network Deployments: A Representative Ground Truth

Modeling Anomalies Prevalent In Sensor Network Deployments: ...

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IEEE 21st Annual International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication (MASCOTS)

作者： Abuaitah, Giovani Rimon Wang, Bin Wright State Univ BMW Networking Res Lab Dayton OH 45435 USA

ISBN: (纸本)9780769551029

The performance of anomaly detection algorithms is usually measured using the total residual error. This error metric is calculated by comparing the labels assigned by the detection algorithm against a reference ground truth. Obtaining a highly expressive ground truth is by itself a challenging task, if not infeasible. Often, a dataset is manually labeled by domain experts. However, manual labeling is error prone. In real-world sensor network deployments, it becomes even more difficult to label a sensor dataset due to the large amount of samples, the complexity of visualizing the data, and the uncertainty in the existence of anomalies. This paper proposes an automated technique which uses highly representative anomaly models for labeling. We demonstrate the effectiveness of this technique through evaluating a classification algorithm using our designed anomaly models as ground truth. We show that the classification accuracy is similar to that when using manually labeled realworld data points.

关键词： anomaly detection automatic labeling data-centric anomalies ground truth sensor networks ground truth anomaly detection Sensor networks Detection algorithms error prone Manual handling Automated recording sorting algorithm

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A Novel Capacitor Voltage Balancing Control Strategy for Modular Multilevel Converters (MMC)

A Novel Capacitor Voltage Balancing Control Strategy for Mod...

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International Conference on Electrical Machines and Systems (ICEMS)

作者： Wang, Lin Wang, Ping Li, Zixin Li, Yaohua Chinese Acad Sci Inst Elect Engn Key Lab Power Elect & Elect Dr Beijing 100190 Peoples R China

Recently modular multilevel converters are highly attractive for medium, high-voltage power transition and electrical machine drive. Capacitor voltage sorting is very important for capacitor voltage-balancing control.... 详细信息

ISBN: (纸本)9781479914463;9781479914470

关键词： modular multilevel converters (MMC) voltage-balancing control sorting algorithm

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Fault Detection in Kerman Combined Cycle Power Plant Boilers by Means of Support Vector Machine Classifier algorithms and PCA 3

Fault Detection in Kerman Combined Cycle Power Plant Boilers...

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3rd International Conference on Control, Instrumentation, and Automation (ICCIA)

作者： Berahman, M. Safavi, A. A. Shahrbabaki, M. Rostami Kerman Combined Cycle Power Plant Kerman Iran Shiraz Univ Power & Control Engn Dept Shiraz Iran

ISBN: (纸本)9781479931170

In this paper, fault detection in HP drum of boilers in Kerman combined cycle power plant is explored by means of support vector machine (SVM) algorithm and principal component analysis (PCA). Initially, SVM classifier algorithm and PCA are discussed and then based on the collecting data on normal and abnormal operating the conditions of boilers, fault detection is carried out via explained methods. Finally, a comparison of these techniques and other routine methods is made to show the superiority with the proposed approaches in Kerman power plant.

关键词： Boilers Classification algorithms Fault detection Indexes Power generation Principal component analysis Support vector machines Boilers Defect detection principal components analysis Support Vector Network sorting algorithm boilers Power production Combined cycle power plants Passive Cutaneous Anaphylaxis

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Practical Linear-Time O(1)-Workspace Suffix sorting for Constant Alphabets

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2013年第3期31卷 15-15页

作者： Nong, Ge Sun Yat Sen Univ Dept Comp Sci Guangzhou 510275 Guangdong Peoples R China

This article presents an O(n)-time algorithm called SACA-K for sorting the suffixes of an input string T[0, n-1] over an alphabet A[0, K-1]. The problem of sorting the suffixes of T is also known as constructing the suffix array (SA) for T. The theoretical memory usage of SACA-K is n log K+n log n+K log n bits. Moreover, we also have a practical implementation for SACA-K that uses n bytes +(n + 256) words and is suitable for strings over any alphabet up to full ASCII, where a word is log n bits. In our experiment, SACA-K outperforms SA-IS that was previously the most time-and space-efficient linear-time SA construction algorithm (SACA). SACA-K is around 33% faster and uses a smaller deterministic workspace of K words, where the workspace is the space needed beyond the input string and the output SA. Given K = O(1), SACA-K runs in linear time and O(1) workspace. To the best of our knowledge, such a result is the first reported in the literature with a practical source code publicly available.

关键词： algorithms Performance Suffix array sorting algorithm linear time O(1)-workspace

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Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

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JOURNAL OF APPLIED MATHEMATICS 2013年第unknown期2013卷 590614-1-590614-18页

作者： Fong, Simon Zhuang, Yan Tang, Rui Yang, Xin-She Deb, Suash Univ Macau Dept Comp & Informat Sci Macau Peoples R China Middlesex Univ Fac Sci & Technol London N17 8HR England Cambridge Inst Technol Dept Comp Sci & Engn Ranchi Bihar India

Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.

关键词： Selecting Optimal Feature Set High-Dimensional Data Swarm Search sorting algorithm Searches Nondeterministic polynomial-time hard dimensional data PLUGGING dike swarms Feature extraction

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EVALUATION OF RULE-BASED CLASSIFIER FOR LANDSAT-BASED AUTOMATED LAND COVER MAPPING IN SOUTH AFRICA

EVALUATION OF RULE-BASED CLASSIFIER FOR LANDSAT-BASED AUTOMA...

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IEEE International Geoscience and Remote Sensing Symposium

作者： B. P. Salmon K. J. Wessels F. van den Bergh K. Steenkamp W. Kleynhans D. Swanepoel D. Roy V. Kovalskyy Remote Sensing Research Unit Meraka Institute CSIR South Africa Geographic Information Science Center of Excellence South Dakota State University South Dakota USA

ISBN: (纸本)9781479911127

This study investigated the automated pre-processing and land cover classification of Landsat data. The Web-enabled Landsat Data (WELD) system was used to process large volumes of Landsat imagery to calibrated top of atmosphere reflectance and brightness temperature products which are composited temporally and mosaicked for the KwaZulu-Natal Province of South Africa. The usefulness of an Automatic Spectral Rule-base Classifier (ASRC) approach was evaluated by relating the produced spectral categories to land cover classes. The ASRC method uses a hierarchical rule set, which relies on universally set thresholds derived from the literature, to decide on the spectral category. To assess the performance, the spectral categories were treated as input features to supervised classifiers to optimally assign land cover labels. The land cover classes used in the experiments were obtained from the official map of the Kwazulu-Natal province in South Africa, which was generated by operators in 2008. This approach was compared to an experiment using the original 7 Landsat spectral bands and derived indices as input features. It was found that the ASRC spectral categories did not provide a useful translation to land cover classes (45.5% classification accuracy), while the experiments using the Landsat 7 spectral bands or indices did considerably better (82.7% classification accuracy).

关键词： Classification algorithms Knowledge based systems Pattern recognition Remote sensing Satellites Knowledge based systems South Africa sorting algorithm satellites Northern Province South Africa Cape Province region Pattern recognition land cover classes remote sensing Landsat satellites land cover Automation Ruleset ENTERING

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K-CENTROIDS-BASED SUPERVISED CLASSIFICATION OF TEXTURE IMAGES: HANDLING THE INTRA-CLASS DIVERSITY

K-CENTROIDS-BASED SUPERVISED CLASSIFICATION OF TEXTURE IMAGE...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Aurelien Schutz Lionel Bombrun Yannick Berthoumieu Universite de Bordeaux ENSEIRB-Matmeca Laboratoire IMS Groupe Signal et Image

ISBN: (纸本)9781479903573

Natural texture images exhibit a high intra-class diversity due to different acquisition conditions (scene enlightenment, perspective angle,...). To handle with the diversity, a new supervised classification algorithm based on a parametric formalism is introduced: the K-centroids-based classifier (K-CB). A comparative study between various supervised classification algorithms on the VisTex and Brodatz image databases is conducted and reveals that the proposed K-CB classifier obtains relatively good classification accuracy with a low computational complexity.

关键词： sorting algorithm GRAIN PATTERN Taxonomy images complexity classes Classifiers textures visual databases modern history

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COMPETITIVE AND ONLINE PIECEWISE LINEAR CLASSIFICATION 38th

COMPETITIVE AND ONLINE PIECEWISE LINEAR CLASSIFICATION

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Huseyin Ozkan Mehmet A. Donmez Ozgun S. Pelvan Arda Akman Suleyman S. Kozat Bilkent University Electrical and Electronics Engineering Department Koc University Electrical and Electronics Engineering Department Turk Telekom Group R&D

ISBN: (纸本)9781479903573

In this paper, we study the binary classification problem in machine learning and introduce a novel classification algorithm based on the "Context Tree Weighting Method". The introduced algorithm incrementally learns a classification model through sequential updates in the course of a given data stream, i.e., each data point is processed only once and forgotten after the classifier is updated, and asymptotically achieves the performance of the best piecewise linear classifiers defined by the "context tree". Since the computational complexity is only linear in the depth of the context tree, our algorithm is highly scalable and appropriate for real time processing. We present experimental results on several benchmark data sets and demonstrate that our method provides significant computational improvement both in the test (5 ～ 35×) and training phases (40 ～ 1000×), while achieving high classification accuracy in comparison to the SVM with RBF kernel.

关键词： Online Competitive Classification Piecewise linear Context tree LDA Classifiers Taxonomy Anterior interventricular branch of left coronary artery real-time process laser Doppler anemometry Left-sided displacement of abomasum Dataset sorting algorithm Support Vector Network

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