共查询到20条相似文献,搜索用时 6 毫秒
1.
B. Samanta K. R. Al-Balushi S. A. Al-Araimi 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2006,10(3):264-271
A study is presented to compare the performance of three types of artificial neural network (ANN), namely, multi layer perceptron
(MLP), radial basis function (RBF) network and probabilistic neural network (PNN), for bearing fault detection. Features are
extracted from time domain vibration signals, without and with preprocessing, of a rotating machine with normal and defective
bearings. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF and PNN for two- class (normal
or fault) recognition. Genetic algorithms (GAs) have been used to select the characteristic parameters of the classifiers
and the input features. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions.
The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of
a rotating machine. The roles of different vibration signals and preprocessing techniques are investigated. The results show
the effectiveness of the features and the classifiers in detection of machine condition. 相似文献
2.
An automated approach to degradation analysis is proposed that uses a rotating machine’s acoustic signal to determine Remaining
Useful Life (RUL). High resolution spectral features are extracted from the acoustic data collected over the entire lifetime
of the machine. A novel approach to the computation of Mutual Information based Feature Subset Selection is applied, to remove
redundant and irrelevant features, that does not require class label boundaries of the dataset or spectral locations of developing
defect to be known or pre-estimated. Using subsets of the feature space, multi-class linear and Radial Basis Function (RBF)
Support Vector Machine (SVM) classifiers are developed and a comparison of their performance is provided. Performance of all
classifiers is found to be very high, 85 to 98%, with RBF SVMs outperforming linear SVMs when a smaller number of features
are used. As larger numbers of features are used for classification, the problem space becomes more linearly separable and
the linear SVMs are shown to have comparable performance. A detailed analysis of the misclassifications is provided and an
approach to better understand and interpret costly misclassifications is discussed. While defining class label boundaries
using an automated k-means clustering algorithm improves performance with an accuracy of approximately 99%, further analysis
shows that in 88% of all misclassifications the actual class of failure had the next highest probability of occurring. Thus,
a system that incorporates probability distributions as a measure of confidence for the predicted RUL would provide additional
valuable information for scheduling preventative maintenance.
This work was supported by IDA Ireland. 相似文献
3.
Felipe Alonso-Atienza José Luis Rojo-ÁlvarezAlfredo Rosado-Muñoz Juan J. VinagreArcadi García-Alberola Gustavo Camps-Valls 《Expert systems with applications》2012,39(2):1956-1967
Early detection of ventricular fibrillation (VF) is crucial for the success of the defibrillation therapy in automatic devices. A high number of detectors have been proposed based on temporal, spectral, and time-frequency parameters extracted from the surface electrocardiogram (ECG), showing always a limited performance. The combination ECG parameters on different domain (time, frequency, and time-frequency) using machine learning algorithms has been used to improve detection efficiency. However, the potential utilization of a wide number of parameters benefiting machine learning schemes has raised the need of efficient feature selection (FS) procedures. In this study, we propose a novel FS algorithm based on support vector machines (SVM) classifiers and bootstrap resampling (BR) techniques. We define a backward FS procedure that relies on evaluating changes in SVM performance when removing features from the input space. This evaluation is achieved according to a nonparametric statistic based on BR. After simulation studies, we benchmark the performance of our FS algorithm in AHA and MIT-BIH ECG databases. Our results show that the proposed FS algorithm outperforms the recursive feature elimination method in synthetic examples, and that the VF detector performance improves with the reduced feature set. 相似文献
4.
Minh Hoai Nguyen Author Vitae Fernando de la Torre Author Vitae 《Pattern recognition》2010,43(3):584-591
Selecting relevant features for support vector machine (SVM) classifiers is important for a variety of reasons such as generalization performance, computational efficiency, and feature interpretability. Traditional SVM approaches to feature selection typically extract features and learn SVM parameters independently. Independently performing these two steps might result in a loss of information related to the classification process. This paper proposes a convex energy-based framework to jointly perform feature selection and SVM parameter learning for linear and non-linear kernels. Experiments on various databases show significant reduction of features used while maintaining classification performance. 相似文献
5.
针对二类支持向量机分类器在隐秘图像检测中训练步骤复杂与推广性弱的缺点,提出了一种新的基于遗传算法和一类支持向量机的隐秘图像检测方案。采用遗传算法进行图像特征选择,一类支持向量机作为分类器。实验结果表明,与只利用一类支持向量机分类,但未进行特征选择的隐秘检测方法相比,提高了隐秘图像检测的识别率和系统检测效率。 相似文献
6.
Simultaneous feature selection and classification using kernel-penalized support vector machines 总被引:2,自引:0,他引:2
We introduce an embedded method that simultaneously selects relevant features during classifier construction by penalizing each feature’s use in the dual formulation of support vector machines (SVM). This approach called kernel-penalized SVM (KP-SVM) optimizes the shape of an anisotropic RBF Kernel eliminating features that have low relevance for the classifier. Additionally, KP-SVM employs an explicit stopping condition, avoiding the elimination of features that would negatively affect the classifier’s performance. We performed experiments on four real-world benchmark problems comparing our approach with well-known feature selection techniques. KP-SVM outperformed the alternative approaches and determined consistently fewer relevant features. 相似文献
7.
Credit scoring using support vector machines with direct search for parameters selection 总被引:1,自引:1,他引:0
Ligang Zhou Kin Keung Lai Lean Yu 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2009,13(2):149-155
Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the
model depends on its parameters’ setting. In this study, we use direct search method to optimize the SVM-based credit scoring
model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment
(DOE) and genetic algorithm (GA). Two real-world credit datasets are selected to demonstrate the effectiveness and feasibility
of the method. The results show that the direct search method can find the effective model with high classification accuracy
and good robustness and keep less dependency on the initial search space or point setting. 相似文献
8.
We present a two-step method to speed-up object detection systems in computer vision that use support vector machines as classifiers. In the first step we build a hierarchy of classifiers. On the bottom level, a simple and fast linear classifier analyzes the whole image and rejects large parts of the background. On the top level, a slower but more accurate classifier performs the final detection. We propose a new method for automatically building and training a hierarchy of classifiers. In the second step we apply feature reduction to the top level classifier by choosing relevant image features according to a measure derived from statistical learning theory. Experiments with a face detection system show that combining feature reduction with hierarchical classification leads to a speed-up by a factor of 335 with similar classification performance. 相似文献
9.
Yi Liu Author Vitae Author Vitae 《Pattern recognition》2006,39(7):1333-1345
In many pattern recognition applications, high-dimensional feature vectors impose a high computational cost as well as the risk of “overfitting”. Feature Selection addresses the dimensionality reduction problem by determining a subset of available features which is most essential for classification. This paper presents a novel feature selection method named filtered and supported sequential forward search (FS_SFS) in the context of support vector machines (SVM). In comparison with conventional wrapper methods that employ the SFS strategy, FS_SFS has two important properties to reduce the time of computation. First, it dynamically maintains a subset of samples for the training of SVM. Because not all the available samples participate in the training process, the computational cost to obtain a single SVM classifier is decreased. Secondly, a new criterion, which takes into consideration both the discriminant ability of individual features and the correlation between them, is proposed to effectively filter out nonessential features. As a result, the total number of training is significantly reduced and the overfitting problem is alleviated. The proposed approach is tested on both synthetic and real data to demonstrate its effectiveness and efficiency. 相似文献
10.
Support vector machines with genetic fuzzy feature transformation for biomedical data classification
In this paper, we present a genetic fuzzy feature transformation method for support vector machines (SVMs) to do more accurate data classification. Given data are first transformed into a high feature space by a fuzzy system, and then SVMs are used to map data into a higher feature space and then construct the hyperplane to make a final decision. Genetic algorithms are used to optimize the fuzzy feature transformation so as to use the newly generated features to help SVMs do more accurate biomedical data classification under uncertainty. The experimental results show that the new genetic fuzzy SVMs have better generalization abilities than the traditional SVMs in terms of prediction accuracy. 相似文献
11.
Yuan YaoAuthor VitaeMassimiliano PontilAuthor Vitae Paolo FrasconiAuthor VitaeFabio RoliAuthor Vitae 《Pattern recognition》2003,36(2):397-406
We present new fingerprint classification algorithms based on two machine learning approaches: support vector machines (SVMs) and recursive neural networks (RNNs). RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features of the fingerprint which can be integrated in the SVM. SVMs are combined with a new error-correcting code scheme. This approach has two main advantages: (a) It can tolerate the presence of ambiguous fingerprint images in the training set and (b) it can effectively identify the most difficult fingerprint images in the test set. By rejecting these images the accuracy of the system improves significantly. We report experiments on the fingerprint database NIST-4. Our best classification accuracy is of 95.6 percent at 20 percent rejection rate and is obtained by training SVMs on both FingerCode and RNN-extracted features. This result indicates the benefit of integrating global and structured representations and suggests that SVMs are a promising approach for fingerprint classification. 相似文献
12.
The pulse-coupled neural network (PCNN) has been widely used in image processing. The outputs of PCNN represent unique features of original stimulus and are invariant to translation, rotation, scaling and distortion, which is particularly suitable for feature extraction. In this paper, PCNN and intersecting cortical model (ICM), which is a simplified version of PCNN model, are applied to extract geometrical changes of rotation and scale invariant texture features, then an one-class support vector machine based classification method is employed to train and predict the features. The experimental results show that the pulse features outperform of the classic Gabor features in aspects of both feature extraction time and retrieval accuracy, and the proposed one-class support vector machine based retrieval system is more accurate and robust to geometrical changes than the traditional Euclidean distance based system. 相似文献
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15.
One of the most powerful, popular and accurate classification techniques is support vector machines (SVMs). In this work, we want to evaluate whether the accuracy of SVMs can be further improved using training set selection (TSS), where only a subset of training instances is used to build the SVM model. By contrast to existing approaches, we focus on wrapper TSS techniques, where candidate subsets of training instances are evaluated using the SVM training accuracy. We consider five wrapper TSS strategies and show that those based on evolutionary approaches can significantly improve the accuracy of SVMs. 相似文献
16.
Short-term fault prediction based on support vector machines with parameter optimization by evolution strategy 总被引:3,自引:0,他引:3
Support vector machines (SVMs) are the effective machine-learning methods based on the structural risk minimization (SRM) principle, which is an approach to minimize the upper bound risk functional related to the generalization performance. The parameter selection is an important factor that impacts the performance of SVMs. Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) is an evolutionary optimization strategy, which is used to optimize the parameters of SVMs in this paper. Compared with the traditional SVMs, the optimal SVMs using CMA-ES have more accuracy in predicting the Lorenz signal. The industry case illustrates that the proposed method is very successfully in forecasting the short-term fault of large machinery. 相似文献
17.
Mohsen Behzad Keyvan Asghari Morteza Eazi Maziar Palhang 《Expert systems with applications》2009,36(4):7624-7629
Effective one-day lead runoff prediction is one of the significant aspects of successful water resources management in arid region. For instance, reservoir and hydropower systems call for real-time or on-line site-specific forecasting of the runoff. In this research, we present a new data-driven model called support vector machines (SVMs) based on structural risk minimization principle, which minimizes a bound on a generalized risk (error), as opposed to the empirical risk minimization principle exploited by conventional regression techniques (e.g. ANNs). Thus, this stat-of-the-art methodology for prediction combines excellent generalization property and sparse representation that lead SVMs to be a very promising forecasting method. Further, SVM makes use of a convex quadratic optimization problem; hence, the solution is always unique and globally optimal. To demonstrate the aforementioned forecasting capability of SVM, one-day lead stream flow of Bakhtiyari River in Iran was predicted using the local climate and rainfall data. Moreover, the results were compared with those of ANN and ANN integrated with genetic algorithms (ANN-GA) models. The improvements in root mean squared error (RMSE) and squared correlation coefficient (R2) by SVM over both ANN models indicate that the prediction accuracy of SVM is at least as good as that of those models, yet in some cases actually better, as well as forecasting of high-value discharges. 相似文献
18.
Chih-Fong Tsai 《Expert Systems》2008,25(4):380-393
Abstract: Bankruptcy prediction and credit scoring are the two important problems facing financial decision support. The multilayer perceptron (MLP) network has shown its applicability to these problems and its performance is usually superior to those of other traditional statistical models. Support vector machines (SVMs) are the core machine learning techniques and have been used to compare with MLP as the benchmark. However, the performance of SVMs is not fully understood in the literature because an insufficient number of data sets is considered and different kernel functions are used to train the SVMs. In this paper, four public data sets are used. In particular, three different sizes of training and testing data in each of the four data sets are considered (i.e. 3:7, 1:1 and 7:3) in order to examine and fully understand the performance of SVMs. For SVM model construction, the linear, radial basis function and polynomial kernel functions are used to construct the SVMs. Using MLP as the benchmark, the SVM classifier only performs better in one of the four data sets. On the other hand, the prediction results of the MLP and SVM classifiers are not significantly different for the three different sizes of training and testing data. 相似文献
19.
Hong Qiao 《Pattern recognition》2007,40(9):2543-2549
Support vector machines (SVMs) are a new and important tool in data classification. Recently much attention has been devoted to large scale data classifications where decomposition methods for SVMs play an important role.So far, several decomposition algorithms for SVMs have been proposed and applied in practice. The algorithms proposed recently and based on rate certifying pair/set provide very attractive features compared with many other decomposition algorithms. They converge not only with finite termination but also in polynomial time. However, it is difficult to reach a good balance between low computational cost and fast convergence.In this paper, we propose a new simple decomposition algorithm based on a new philosophy on working set selection. It has been proven that the working set selected by the new algorithm is a rate certifying set. Further, compared with the existing algorithms based on rate certifying pair/set, our algorithm provides a very good feature in combination of lower computational complexity and faster convergence. 相似文献
20.
Support Vector Machine (SVM) employs Structural Risk Minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization (ERM) principle. When applying SVM to response modeling in direct marketing, however, one has to deal with the practical difficulties: large training data, class imbalance and scoring from binary SVM output. For the first difficulty, we propose a way to alleviate or solve it through a novel informative sampling. For the latter two difficulties, we provide guidelines within SVM framework so that one can readily use the paper as a quick reference for SVM response modeling: use of different costs for different classes and use of distance to decision boundary, respectively. This paper also provides various evaluation measures for response models in terms of accuracies, lift chart analysis, and computational efficiency. 相似文献