An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging,Boosting, and Randomization

Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a base learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper compares the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than boosting, and sometimes better than randomization.     

网页文本的组合分类研究

在文本分类中,单分器具有某些缺点,如分类模型对样本具有敏感性,并且其分类精度难以提高.本文以朴素贝叶斯为基分类器,利用bagging与boosting技术提出了两种组合分类算法,并将其用于网页文本的自动分类,结果证明组合分类系统提高了分类准确率.     

基于集成学习理论的文本情感分类

随着Web2.0的迅速发展,越来越多的用户乐于在互联网上分享自己的观点或体验。这类评论信息迅速膨胀,仅靠人工的方法难以应对网上海量信息的收集和处理,因此基于计算机的文本情感分类技术应运而生,并且研究的重点之一就是提高分类的精度。由于集成学习理论是提高分类精度的一种有效途径,并且已在许多领域显示出其优于单个分类器的良好性能,为此,提出基于集成学习理论的文本情感分类方法。实验结果显示三种常用的集成学习方法 Bagging、Boosting和Random Subspace对基础分类器的分类精度都有提高,并且在不同的基础分类器条件下,Random Subspace方法较Bagging和Boosting方法在统计意义上更优,以上结果进一步验证了集成学习理论在文本情感分类中应用的有效性。     

A feature selection enabled hybrid‐bagging algorithm for credit risk evaluation

Hybrid models based on feature selection and machine learning techniques have significantly enhanced the accuracy of standalone models. This paper presents a feature selection‐based hybrid‐bagging algorithm (FS‐HB) for improved credit risk evaluation. The 2 feature selection methods chi‐square and principal component analysis were used for ranking and selecting the important features from the datasets. The classifiers were built on 5 training and test data partitions of the input data set. The performance of the hybrid algorithm was compared with that of the standalone classifiers: feature selection‐based classifiers and bagging. The hybrid FS‐HB algorithm performed best for qualitative dataset with less features and tree‐based unstable base classifier. Its performance on numeric data was also better than other standalone classifiers, whereas comparable to bagging with only selected features. Its performance was found better on 70:30 data partition and the type II error, which is very significant in risk evaluation was also reduced significantly. The improved performance of FS‐HB is attributed to the important features used for developing the classifier thereby reducing the complexity of the algorithm and the use of ensemble methodology, which added to the classical bias variance trade‐off and performed better than standalone classifiers.     

拥挤场景下的暴力行为检测

近年来暴力行为检测受到了越来越多的关注,而在图像研究领域中,这仍然是一个充满挑战的话题。尝试对现有的暴力行为检测器进行改进,提出了一个全新的特征构造方法,称为加速度暴力流(Acceleration Violent Flow,简称AViF),它充分利用光流信息中的加速度信息,可以用来检测拥挤场景下的暴力行为;之后采用了Bagging策略,取得了良好的效果。实验结果表明,在bagging策略下,组合特征取得了高于ViF特征的效果。     

基于选择迁移的baggi ng文本分类算法

针对目标域训练样本数量较少无法建立优质分类模型的问题,提出一种在迁移框架下基于集成bagging算法的跨领域分类方法。引入源域的数据并对其进行筛选,对混合数据集进行学习,建立基于集成bagging算法的分类模型,投票得出预测结果。仿真对比结果表明,采用基于贝叶斯个体分类器的集成bagging算法能够优化源域的迁移,提升目标域的分类准确率及泛化性能。分析源域的噪音数据数量,其结果表明,该算法可以部分规避负迁移。     

全自动包装码垛生产线控制系统设计

介绍了全自动包装码垛生产线工艺流程,设计了以日本欧姆龙公司C200H型PLC为基础的全自动包装码垛生产线控制系统,重点介绍了控制系统的硬件配置、输入输出点分配和软件设计.     

MultiBoosting: A Technique for Combining Boosting and Wagging

MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, MultiBoosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution.     

阀口袋套袋机器人轨迹优化方法研究

目的 为了提高阀口袋套袋机器人的工作效率,提出基于改进粒子群算法的阀口袋套袋机器人时间最优轨迹规划方法。方法 首先,通过逆运动学方程反解出操作空间轨迹对应的关节空间角度值。其次,采用4–3–4混合多项式对阀口袋套袋机器人的关节空间轨迹进行插值拟合。最后,在速度约束条件下,利用改进粒子群算法对阀口袋套袋机器人的运行时间进行优化处理。结果 仿真结果表明,改进粒子群算法可以在保证阀口袋套袋机器人运行平稳的条件下将总运行时间缩减41.66%,实现了阀口袋套袋机器人在关节空间中时间最优的轨迹规划。结论 该方法可有效地提高机器人工作效率,延长机器人的使用寿命,为阀口袋套袋机器人稳定可靠运行提供了科学依据。     

采后过氧化氢处理对伽师瓜贮藏品质的影响

以新疆伽师瓜为实验材料,研究冷藏(6±1)℃条件下,不同浓度H2O2处理结合处理后套袋对伽师瓜果实采后贮藏品质的影响。结果表明：贮藏期间,过氧化氢处理可明显延缓伽师瓜硬度下降,降低呼吸、乙烯高峰和贮藏过程中的水分损失,减缓可溶性固形物及VC含量的消耗,而处理后套袋可明显延迟呼吸和乙烯高峰。可见处理保持了贮藏期伽师瓜的品质,有利于伽师瓜的贮藏。综合比较,4% H2O2处理后结合套袋对伽师瓜贮藏期品质保持的效果最佳。