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Ensemble pruning deals with the selection of base learners prior to combination in order to improve prediction accuracy and efficiency. In the ensemble literature, it has been pointed out that in order for an ensemble classifier to achieve higher prediction accuracy, it is critical for the ensemble classifier to consist of accurate classifiers which at the same time diverse as much as possible. In this paper, a novel ensemble pruning method, called PL-bagging, is proposed. In order to attain the balance between diversity and accuracy of base learners, PL-bagging employs positive Lasso to assign weights to base learners in the combination step. Simulation studies and theoretical investigation showed that PL-bagging filters out redundant base learners while it assigns higher weights to more accurate base learners. Such improved weighting scheme of PL-bagging further results in higher classification accuracy and the improvement becomes even more significant as the ensemble size increases. The performance of PL-bagging was compared with state-of-the-art ensemble pruning methods for aggregation of bootstrapped base learners using 22 real and 4 synthetic datasets. The results indicate that PL-bagging significantly outperforms state-of-the-art ensemble pruning methods such as Boosting-based pruning and Trimmed bagging. 相似文献
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乔石 《太原重型机械学院学报》2009,(6):476-479
提出了一种样本间的相似性度量方法,并将这种相似性度量信息附加到Fisher线性判别的类内、类间离散度矩阵,使得Fisher判决准则在使类内距离迭最小、类间距离迭最大的同时,也使类内相似度迭最小、类间相似度达最大,获得比原始Fisher判剐更好的投影矩阵。实验证明,与Bagging集成的Fisherfaee比较,该方法显示出更好的识别率。 相似文献
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基于Bagging算法和遗传神经网络的交通事件检测 总被引:1,自引:0,他引:1
朱红斌 《计算机应用与软件》2010,27(1):234-236
提出一种集成遗传神经网络的交通事件检测方法,以上下游的流量和占有率作为特征,RBF神经网络作为分类器进行交通事件的自动分类与检测。在RBF神经网络的训练过程中,采用遗传算法GA(Genetic Algorithm)对RBF神经网络的隐层中心值和宽度进行优化,用递推最小二乘法训练隐层和输出层之间的权值。为了提高神经网络的分类能力,采用Bagging算法,进行网络集成。通过Matlab仿真实验,证明该方法相对于传统的事件检测算法能更准确、快速地实现分类。 相似文献
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Learning from imperfect (noisy) information sources is a challenging and reality issue for many data mining applications. Common practices include data quality enhancement by applying data preprocessing techniques or employing robust learning algorithms to avoid developing overly complicated structures that overfit the noise. The essential goal is to reduce noise impact and eventually enhance the learners built from noise-corrupted data. In this paper, we propose a novel corrective classification (C2) design, which incorporates data cleansing, error correction, Bootstrap sampling and classifier ensembling for effective learning from noisy data sources. C2 differs from existing classifier ensembling or robust learning algorithms in two aspects. On one hand, a set of diverse base learners of C2 constituting the ensemble are constructed via a Bootstrap sampling process; on the other hand, C2 further improves each base learner by unifying error detection, correction and data cleansing to reduce noise impact. Being corrective, the classifier ensemble is built from data preprocessed/corrected by the data cleansing and correcting modules. Experimental comparisons demonstrate that C2 is not only more accurate than the learner built from original noisy sources, but also more reliable than Bagging [4] or aggressive classifier ensemble (ACE) [56], which are two degenerated components/variants of C2. The comparisons also indicate that C2 is more stable than Boosting and DECORATE, which are two state-of-the-art ensembling methods. For real-world imperfect information sources (i.e. noisy training and/or test data), C2 is able to deliver more accurate and reliable prediction models than its other peers can offer. 相似文献
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Several tools are sold and recommended for closing and sealing flexible intermediate bulk containers (bulk bags) which are used to transport product that has been mined and processed. However, there is limited information on the risks, physical demands, or the benefits of using one tool over another. The purpose of this study was to evaluate the physical demands involved with two closing methods and several sealing tools in order to provide recommendations for selecting tools to reduce exposure to risk factors for work-related musculoskeletal disorders. In this study, twelve participants completed bag closing and sealing tasks using two different closing methods and eight sealing tools on two types of bulk bags. Physical demands and performance were evaluated using muscle activity, perceived exertion, subjective ratings of use, and time. Results indicate that using the “flowering” method to close bags required on average 32% less muscle activity, 30% less perceived exertion, 42% less time, and was preferred by participants compared to using the “snaking” method. For sealing, there was no single method significantly better across all measures; however, using a pneumatic cable tie gun consistently had the lowest muscle activity and perceived exertion ratings. The pneumatic cable tie gun did require approximately 33% more time to seal the bag compared to methods without a tool, but the amount of time to seal the bag was comparable to using other tools. Further, sealing a spout bulk bag required on average 13% less muscle activity, 18% less perceived exertion, 35% less time, and was preferred by participants compared to sealing a duffle bulk bag. The current results suggest that closing the spout bag using the flowering method and sealing the bag using the pneumatic cable tie gun that is installed with a tool balancer is ergonomically advantageous. Our findings can help organizations select methods and tools that pose the lowest physical demands when closing and sealing bulk bags. 相似文献
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Gonzalo Martínez-Muñoz Author Vitae Alberto Suárez Author Vitae 《Pattern recognition》2005,38(10):1483-1494
Ensembles that combine the decisions of classifiers generated by using perturbed versions of the training set where the classes of the training examples are randomly switched can produce a significant error reduction, provided that large numbers of units and high class switching rates are used. The classifiers generated by this procedure have statistically uncorrelated errors in the training set. Hence, the ensembles they form exhibit a similar dependence of the training error on ensemble size, independently of the classification problem. In particular, for binary classification problems, the classification performance of the ensemble on the training data can be analysed in terms of a Bernoulli process. Experiments on several UCI datasets demonstrate the improvements in classification accuracy that can be obtained using these class-switching ensembles. 相似文献
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An Efficient Method To Estimate Bagging's Generalization Error 总被引:3,自引:0,他引:3
Bagging (Breiman, 1994a) is a technique that tries to improve a learning algorithm's performance by using bootstrap replicates of the training set (Efron & Tibshirani, 1993, Efron, 1979). The computational requirements for estimating the resultant generalization error on a test set by means of cross-validation are often prohibitive, for leave-one-out cross-validation one needs to train the underlying algorithm on the order of m times, where m is the size of the training set and is the number of replicates. This paper presents several techniques for estimating the generalization error of a bagged learning algorithm without invoking yet more training of the underlying learning algorithm (beyond that of the bagging itself), as is required by cross-validation-based estimation. These techniques all exploit the bias-variance decomposition (Geman, Bienenstock & Doursat, 1992, Wolpert, 1996). The best of our estimators also exploits stacking (Wolpert, 1992). In a set of experiments reported here, it was found to be more accurate than both the alternative cross-validation-based estimator of the bagged algorithm's error and the cross-validation-based estimator of the underlying algorithm's error. This improvement was particularly pronounced for small test sets. This suggests a novel justification for using bagging—more accurate estimation of the generalization error than is possible without bagging. 相似文献