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54th ELMAR International Symposium

作者： Vavrek, Jozef Cizmar, Anton Juhar, Jozef Tech Univ Kosice Letna 9 Kosice 04200 Slovakia

ISBN: (纸本)9789537044138

The paper presents the support vector machine binary decision tree scheme (SVM-BDT) used for broadcast news (BN) audio classification. The SVM-BDT architecture was designed to solve multi-class discrimination problem of considered acoustic events: pure speech, speech with music, speech with environment sound, music, and environment sound. Its performance was investigated by using Mel-frequency cepstral coefficients (MFCCs), as a powerful signal parameterization technique, for each SVM binary classifier. The one-against-all strategy in combination with Euclidean distance algorithm was implemented in discrimination process, in order to decrease the influence of missclassification between each class.

关键词： SVM distance measure classification algorithm BN audio stream

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User preferences based software defect detection algorithms selection using MCDM

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INFORMATION SCIENCES 2012年 191卷 3-13页

作者： Peng, Yi Wang, Guoxun Wang, Honggang Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610054 Peoples R China Univ Massachusetts Dept Elect & Comp Engn Dartmouth MA USA

A variety of classification algorithms for software defect detection have been developed over the years. How to select an appropriate classifier for a given task is an important issue in Data mining and knowledge discovery (DMKD). Many studies have compared different types of classification algorithms and the performances of these algorithms may vary using different performance measures and under different circumstances. Since the algorithm selection task needs to examine several criteria, such as accuracy, computational time, and misclassification rate, it can be modeled as a multiple criteria decision making (MCDM) problem. The goal of this paper is to use a set of MCDM methods to rank classification algorithms, with empirical results based on the software defect detection datasets. Since the preferences of the decision maker (DM) play an important role in algorithm evaluation and selection, this paper involved the DM during the ranking procedure by assigning user weights to the performance measures. Four MCDM methods are examined using 38 classification algorithms and 13 evaluation criteria over 10 public-domain software defect datasets. The results indicate that the boosting of CART and the boosting of C4.5 decision tree are ranked as the most appropriate algorithms for software defect datasets. Though the MCDM methods provide some conflicting results for the selected software defect datasets, they agree on most top-ranked classification algorithms. (C) 2010 Elsevier Inc. All rights reserved.

关键词： algorithm selection classification algorithm Knowledge-driven data mining Multi-criteria decision making (MCDM) Software defect detection

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Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy

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LWT-FOOD SCIENCE AND TECHNOLOGY 2011年第10期44卷 2053-2058页

作者： Chen, Quansheng Cai, Jianrong Wan, Xinmin Zhao, Jiewen Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Jiangsu Provinc Peoples R China

To address the rapid and nondestructive determination of pork storage time associated with its freshness, Fourier transform near infrared (FT-NIR) spectroscopy technique, with the help of classification algorithm, was attempted in this work. To investigate the effects of different linear and non-linear classification algorithms on the discrimination results, linear discriminant analysis (LDA), K-nearest neighbors (KNN), and back propagation artificial neural network (BP-ANN) were used to develop the discrimination models, respectively. The number of principal components (PCs) and other parameters were optimized by cross-validation in developing discrimination models. Experimental results showed that the performance of BP-ANN model was superior to others, and the optimal BP-ANN model was achieved when 5 PCs were included. The discrimination rates of the BP-ANN model were 99.26% and 96.21% in the training and prediction sets, respectively. The overall results sufficiently demonstrate that the FT-NIR spectroscopy technique combined with BP-ANN classification algorithm has the potential to determine pork storage time associated with its freshness. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Pork Storage time Determination FT-NIR spectroscopy classification algorithm

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NEW TECHNIQUES FOR IMPROVING THE PRACTICALITY OF AN SVM-BASED SPEECH/MUSIC CLASSIFIER

NEW TECHNIQUES FOR IMPROVING THE PRACTICALITY OF AN SVM-BASE...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Lim, Chungsoo Lee, Seong-Ro Lee, Yeon-Woo Chang, Joon-Hyuk Hanyang Univ Seoul 133791 South Korea Mokpo Natl Univ Mokpo South Korea

ISBN: (纸本)9781467300469

Variable bit-rate coding introduced for effective utilization of limited communication bandwidth requires accurate classification of input signals. This paper investigates implementation of a support vector machine (SVM)-based speech/music classifier in the selectable mode vocoder (SMV) framework, which is a standard codec adopted by the Third-Generation Partnership Project 2 (3GPP2). A support vector machine is well known for its superior pattern recognition capability;however, it is accompanied by a high computational cost. In order to achieve a more practical system, three techniques are proposed for the SVM-based speech/music classifier. The first is to prune support vectors that least contribute to the output of the SVM, while the other two are aimed at reducing the number of classification requests to the SVM-based classifier by eliminating or redirecting some of the classification requests to the classifier.

关键词： Codecs classification algorithm embedded software

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Digital Modulation classification Based on Characteristic Features and GentleBoost algorithm

Digital Modulation Classification Based on Characteristic Fe...

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34th International Conference on Telecommunications and Signal Processing (TSP)

作者： Kubankova, Anna Kubanek, David Prinosil, Jiri Brno Univ Technol Dept Telecommun Fac Elect Engn & Commun Brno Czech Republic

ISBN: (纸本)9781457714115

The paper describes algorithm for the classification of digital modulations and its testing with disturbed signals. 2ASK, 2FSK, 4FSK, MSK, BPSK, QPSK, 8PSK and 16QAM were chosen for recognition as the best-known digital modulations used in modern communication technologies. The method designed uses ten features computed from parameters of recognized signal such as instantaneous amplitude, instantaneous phase, instantaneous frequency and spectrum characteristic. The GentleBoost algorithm was used to analyze the features and classify the modulations. We used multipath fading channel to model signal propagation and disturbed the signal by white Gaussian noise for the purpose of testing the algorithm.

关键词： Recognition of modulations Digital modulation classification algorithm Features extraction

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Minimising Added classification Error Using Walsh Coefficients

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2011年第8期22卷 1334-1339页

作者： Windeatt, Terry Zor, Cemre Univ Surrey Fac Elect & Phys Sci Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

Two-class supervised learning in the context of a classifier ensemble may be formulated as learning an incompletely specified Boolean function, and the associated Walsh coefficients can be estimated without the knowledge of the unspecified patterns. Using an extended version of the Tumer-Ghosh model, the relationship between added classification error and second-order Walsh coefficients is established. In this brief, the ensemble is composed of multilayer perceptron base classifiers, with the number of hidden nodes and epochs systematically varied. Experiments demonstrate that the mean second-order coefficients peak at the same number of training epochs as ensemble test error reaches a minimum.

关键词： classification algorithm multilayer perceptrons pattern analysis pattern recognition

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Stepwise classification of cancer samples using clinical and molecular data

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BMC BIOINFORMATICS 2011年第1期12卷 1-12页

作者： Obulkasim, Askar Meijer, Gerrit A. van de Wiel, Mark A. Vrije Univ Amsterdam Med Ctr Dept Epidemiol & Biostat Amsterdam Netherlands Vrije Univ Amsterdam Med Ctr Dept Pathol Amsterdam Netherlands Vrije Univ Amsterdam Dept Math Amsterdam Netherlands

Background: Combining clinical and molecular data types may potentially improve prediction accuracy of a classifier. However, currently there is a shortage of effective and efficient statistical and bioinformatic tools for true integrative data analysis. Existing integrative classifiers have two main disadvantages: First, coarse combination may lead to subtle contributions of one data type to be overshadowed by more obvious contributions of the other. Second, the need to measure both data types for all patients may be both unpractical and (cost) inefficient. Results: We introduce a novel classification method, a stepwise classifier, which takes advantage of the distinct classification power of clinical data and high-dimensional molecular data. We apply classification algorithms to two data types independently, starting with the traditional clinical risk factors. We only turn to relatively expensive molecular data when the uncertainty of prediction result from clinical data exceeds a predefined limit. Experimental results show that our approach is adaptive: the proportion of samples that needs to be re-classified using molecular data depends on how much we expect the predictive accuracy to increase when re-classifying those samples. Conclusions: Our method renders a more cost-efficient classifier that is at least as good, and sometimes better, than one based on clinical or molecular data alone. Hence our approach is not just a classifier that minimizes a particular loss function. Instead, it aims to be cost-efficient by avoiding molecular tests for a potentially large subgroup of individuals;moreover, for these individuals a test result would be quickly available, which may lead to reduced waiting times (for diagnosis) and hence lower the patients distress. Stepwise classification is implemented in R-package stepwiseCM and available at the Bioconductor website.

关键词： Partial Little Square Data Type Random Forest Molecular Data classification algorithm

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Density-based Hardware-oriented classification for Spike Sorting Microsystems

Density-based Hardware-oriented Classification for Spike Sor...

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5th International IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) Conference on Neural Engineering (NER)

作者： Cheng, Li-Fang Chen, Tung-Chien Chang, Nai-Fu Chen, Liang-Gee Natl Taiwan Univ Dept Elect Engn Taipei 10764 Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei 10764 Taiwan

ISBN: (纸本)9781424441419

Successful proof-of-concept laboratory experiments on cortically-controlled brain computer interface motivate continued development for neural prosthetic microsystems (NPMs). One of the research directions is to realize realtime spike sorting processors (SSPs) on the NPM. The SSP detects the spikes, extracts the features, and then performs the classification algorithm in realtime in order to differentiate the spikes for the different firing neurons. Several architectures have been designed for the spike detection and feature extraction. However, the classification hardware is missing. To complete the SSP, a density-based hardware-oriented classification algorithm is proposed for hardware implementation. The traditional classification algorithms require a considerable memory space to store all the training features during the processing iteration, which results in a considerable power and area for the hardware. The proposed one is designed based on the density map of the spike features. The density map can be accumulated on-line with the coming of the spike features. Therefore the algorithm can save significant memory space, and is good for efficient hardware implementation.

关键词： algorithm design and analysis Clustering algorithms Feature extraction Hardware Memory management Sorting Training brain-computer interfaces classification algorithm cortically-controlled brain computer interface density-based hardware-oriented classification feature extraction feature extraction firing neurons hardware-oriented classification medical signal detection medical signal processing neural prosthetic microsystems neurophysiology prosthetics signal classification spike sorting microsystems spike sorting processors

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Optimal selection of potential customer range through the union sequential pattern by using a response model

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第6期38卷 7451-7461页

作者： Chen, Wen-Chin Hsu, Chiun-Chieh Hsu, Jing-Ning Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei Taiwan Chunghwa Telecom Co Ltd Telecommun Labs Business & Mkt Strategy Res Dept Taipei Taiwan Chunghwa Telecom Co Ltd Data Commun Business Grp Dept Mkt Taipei Taiwan

Direct marketing is a common data mining application. Previous studies largely adopt the approach that, as the subjects for response model predictions, the entire customer population or filtered through certain attribute values is based on recommendations made from sales professionals. However, such methods may reduce response rates due to an oversized potential customer population, thus diminishing the accuracy of the prediction model. In resolve this problem, this work presents proposes a novel forecasting method that integrates the union sequential pattern with classification algorithms to facilitate the construction of customer response models. Based on use of a union sequential pattern, the potential customer size is established by identifying attributes with a high level of association. The prediction model is then constructed using classification algorithms such as support vector machines and logistic regression. Consequently, the problem involving the setting of range for potential customers can be solved, as well as the time spent on processing extended lists of customers during prediction. Finally, predicted potential Internet-phone customers and churning mobile-phone customers of a telecommunication company in Taiwan as are taken as an illustrative example, based on the proposed prediction model. The proposed method more accurately predicts potential customers than those of previous studies. Crown Copyright (c) 2010 Published by Elsevier Ltd. All rights reserved.

关键词： Response model Union sequential pattern classification algorithm Support vector machine Logistic regression

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Software to quantify and map vegetative cover in fallow fields for weed management decisions

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2011年第1期78卷 106-115页

作者： Wiles, L. J. USDA ARS Water Management Res Unit Ft Collins CO 80526 USA

Mapping weed cover during the fallow period of dryland crop rotations would be valuable for weed management in subsequent crops and could be done with low cost color digital cameras, however most managers lack the specialized software and expertise needed to create a map from the images. A system of software was developed to quantify weed cover in fallow fields in digital images and to simplify and automate the most challenging tasks that non-GIS professionals confront in creating and using maps derived from a large number of images. A GIS file of image locations is created with inexpensive consumer software. Images are classified, a GIS file is generated and the map is displayed in a simple GIS viewer with free software we developed. A map can be generated from 1000 images and 5000 GPS coordinates in 30 min, including image classification. The classified and original images for all locations can be viewed together easily from the map application. The accuracy of estimating weed cover was evaluated using images collected in 15 fields under natural light with a consumer grade camera mounted on an ATV driving 8-11 km h(-1). Weed cover was estimated with 96% accuracy for images, regardless of the amount of crop residue, unless part of the image was shaded by the camera. In those images, accuracy was 90% or better. This system will work with many professional and consumer digital cameras and GPS units and the classification algorithm can be easily modified for other applications. Published by Elsevier B.V.

关键词： Digital images Image analysis Weed map classification algorithm Site-specific weed management

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