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101.
Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection
method for the Support Vector Machine (SVM) learning. Like most feature-selection methods, the proposed method ranks all features
in decreasing order of importance so that more relevant features can be identified. It uses a novel criterion based on the
probabilistic outputs of SVM. This criterion, termed Feature-based Sensitivity of Posterior Probabilities (FSPP), evaluates
the importance of a specific feature by computing the aggregate value, over the feature space, of the absolute difference
of the probabilistic outputs of SVM with and without the feature. The exact form of this criterion is not easily computable
and approximation is needed. Four approximations, FSPP1-FSPP4, are proposed for this purpose. The first two approximations
evaluate the criterion by randomly permuting the values of the feature among samples of the training data. They differ in
their choices of the mapping function from standard SVM output to its probabilistic output: FSPP1 uses a simple threshold
function while FSPP2 uses a sigmoid function. The second two directly approximate the criterion but differ in the smoothness
assumptions of criterion with respect to the features. The performance of these approximations, used in an overall feature-selection
scheme, is then evaluated on various artificial problems and real-world problems, including datasets from the recent Neural
Information Processing Systems (NIPS) feature selection competition. FSPP1-3 show good performance consistently with FSPP2
being the best overall by a slight margin. The performance of FSPP2 is competitive with some of the best performing feature-selection
methods in the literature on the datasets that we have tested. Its associated computations are modest and hence it is suitable
as a feature-selection method for SVM applications.
Editor: Risto Miikkulainen. 相似文献
102.
To improve the performance of scientific applications with parallel loops, dynamic loop scheduling methods have been proposed.
Such methods address performance degradations due to load imbalance caused by predictable phenomena like nonuniform data distribution
or algorithmic variance, and unpredictable phenomena such as data access latency or operating system interference. In particular,
methods such as factoring, weighted factoring, adaptive weighted factoring, and adaptive factoring have been developed based
on a probabilistic analysis of parallel loop iterates with variable running times. These methods have been successfully implemented
in a number of applications such as: N-Body and Monte Carlo simulations, computational fluid dynamics, and radar signal processing.
The focus of this paper is on adaptive weighted factoring (AWF), a method that was designed for scheduling parallel loops
in time-stepping scientific applications. The main contribution of the paper is to relax the time-stepping requirement, a
modification that allows the AWF to be used in any application with a parallel loop. The modification further allows the AWF
to adapt to load imbalance that may occur during loop execution. Results of experiments to compare the performance of the
modified AWF with the performance of the other loop scheduling methods in the context of three nontrivial applications reveal
that the performance of the modified method is comparable to, and in some cases, superior to the performance of the most recently
introduced adaptive factoring method.
相似文献
Ioana BanicescuEmail: |
103.
Wei-Chou Chen Shian-Shyong Tseng Tzung-Pei Hong 《Expert systems with applications》2008,34(4):2858-2869
Feature selection is about finding useful (relevant) features to describe an application domain. Selecting relevant and enough features to effectively represent and index the given dataset is an important task to solve the classification and clustering problems intelligently. This task is, however, quite difficult to carry out since it usually needs a very time-consuming search to get the features desired. This paper proposes a bit-based feature selection method to find the smallest feature set to represent the indexes of a given dataset. The proposed approach originates from the bitmap indexing and rough set techniques. It consists of two-phases. In the first phase, the given dataset is transformed into a bitmap indexing matrix with some additional data information. In the second phase, a set of relevant and enough features are selected and used to represent the classification indexes of the given dataset. After the relevant and enough features are selected, they can be judged by the domain expertise and the final feature set of the given dataset is thus proposed. Finally, the experimental results on different data sets also show the efficiency and accuracy of the proposed approach. 相似文献
104.
105.
提出了一种基于支持向量机的改进的降维方法.在输入和特征空间中,特征子集的选取分别根据原始特征每一维对分类的贡献来获得.最后,通过将输入和特征空间中的特征选取联合起来,得到了一种改进的降维方法.实验表明:使用这种方法,在保持对分类准确率不受明显的影响的同时,能大大地提高训练和预测的速度. 相似文献
106.
根据特征编码的思想,首次提出了一种基于文字下划线特征变化的脆弱型文本水印算法.该算法利用字符的内码特征信息生成水印,然后按照特定的顺序插入水印或检测水印.理论分析和实验表明,算法具有良好的不可感知性,能准确地检测各类水印攻击,在大部分情况下能对篡改进行精确定位. 相似文献
107.
张惠春 《数字社区&智能家居》2009,(8)
中文名词短语识别在自然语言处理已经得到了广泛应用。该文首先对名词短语识别问题进行描述,然后利用最大熵模型建立名词短语识别系统,通过实验选取最大熵模型的特征,最后利用选取的特征进行名词短语识别,实验结果表明系统达到了较高的准确率和召回率。 相似文献
108.
本文针对专业全文搜索引擎的特点,设计并实现了一种网络机器人。通过二维矢量工作队列实现站点式深度优先搜索,采用页面站点加权算法动态控制站点的处理时间。完成了网络上与专业相关信息的集中收集和处理,并探讨了网络机器人对专业的侧重性以及向通用全文搜索引擎网络机器人转换的可行性。 相似文献
109.
近年来由于人脸检测技术在身份验证、视频监控等领域日益广泛的应用,对于人脸检测的研究越来越受到人们的重视。该文对各发展阶段的方法进行介绍比较,指出其优劣性。 相似文献
110.
传统的Isomap算法仅侧重于当前数据的分析,不能提供由高维空间到低维空间的快速直接映射,因此无法用于特征提取和高维数据检索.针对这一问题,文中提出一种基于Isornap的快速数据检索算法.该算法能够快速得到新样本的低维嵌入坐标,并基于此坐标检索与输入样本最相似的参考样本.在典型测试集上的实验结果表明,该算法在实现新样本到低维流形快速映射的同时,能较好保留样本的近邻关系. 相似文献