Anti-noise Performance Analysis of Classifiers Ensembles Based on Feature Selection

特征选择有助于增强集成分类器成员间的随机差异性,从而提高泛化精度。研究了随机子空间法(Random Subspace)和旋转森林法(Rotation Forest)两种基于特征选择的集成分类器构造算法,分析讨论了两算法特征选择的方式与随机差异程度之间的关系。通过对UCI数据集引入噪声,比较两者在噪声环境下的分类精度。实验结果表明:当噪声增加及特征关联度下降时,基本学习算法及噪声程度对集成效果均有影响,当噪声增强到一定程度后,集成效果和单分类器的性能趋于一致。Feature selection encourages random differentiation of the members of the ensembles to improve generation accuracy. In this pa per, random subspace and rotation forest, two algorithms based on feature selection for constructing classifiers ensembles were researched and their relationship between ways of selecting features and its affection on diversity was discussed. By introducing noise into UCI data sets,compared anti-noise performance with different noisy level of two algorithms. Experimental results indicate that both base learning algorithms and noisy level affect the accuracy of an ensemble while noise increases and feature correlation decreases. In situation with higher classification noise, both ensembles and single classifier exhibit quite similar performance.广西自然科学基金项目(2010GXNSFA013127);广西教育项目(201106LX131

一种基于泛函网络的多项式Euclidean算法

提出一种基于泛函网络的多项式Euclidean计算新模型,给出一种基于泛函网络的多项式Euclidean新算法。网络的泛函参数利用解线性方程组方法来完成。相对于传统方法,该方法不但能够快速地获得所求多项式问题的精确解,而且可获得所求多项式问题的近似解。计算机仿真结果表明,该算法十分有效、可行,可以看作是对传统的Eu- clidean算法的一种推广。该算法将在计算机数学、代数密码学等方面有着广泛的应用

Algorithm of heterogeneous classifiers ensembles based on DECORATE

在基于Stacking框架下异构分类器集成方式分析的基础上,引入同构分类器集成中改变训练样本以增强成员分类器间差异性的思想,提出融合DECORATE的异构分类器集成算法SDE;在1-层泛化利用DECORATE算法,向1-层训练集增加一定比例的人工数据,使得生成的多个1-层成员分类器间具有差异性。实验表明,该方法在分类精度上要优于传统Stacking方法。Based on the Stacking framework to construct heterogeneous ensembles,this paper introduced manipulating training samples in the context of creating homogeneous ensembles as the mechanism to encourage diversity.It proposed a new algorithm SDE,which used DECORATE to generate level-1 ensembles by adding proportion of artificial data to level-1 training set so as to inject diversity for member classifiers in level-1.Experiment results indicate that the proposed method achieves better performance than classic Stacking.广西自然科学基金资助项目(2010GXNSFA013127);广西教育厅资助项目(201106LX131

Effect of Scout Bees on the Performance of Artificial Bee Colony Algorithm

人工蜂群算法(artificial bee colony algorithm,ABC)是一种模仿蜜蜂采蜜行为的新兴的群智能优化技术。侦察蜂作为人工蜂群的成员之一,进行随机搜索以找到蜜源。为了弄清楚侦察蜂在ABC中的作用,本文首先分析ABC的生物学机理和主要处理步骤,然后研究当问题维数、种群规模、limit值和最大循环次数等4个控制参数取不同值时对无侦察蜂ABC、单侦察蜂ABC与多侦察蜂ABC性能的影响。实验结果表明,在绝大多数情况下,多侦察蜂ABC求解5个著名的基准函数获得的解优于单侦察蜂ABC和无侦察蜂ABC,而单侦察蜂ABC获得的解优于无侦察蜂ABC。此外,由于这3种算法的搜索复杂度是同阶的,在相同条件下其运行时间相差不大,这充分说明了侦察蜂实施随机勘探过程确实对ABC的性能具有积极意义。Artificial Bee Colony （ABC） algorithm is a new swarm intelligence technique inspired by the foraging behavior of a honeybee swarm. As the member of the artificial bee colony,scout bees carry out random search for discovering food sources. Irt order to investigate the effect of scout bees ort the perfor-mance of ABC,the biological mechanism and main steps of ABC were analyzed,and then,different prob-lem dimensions, population sizes, limit values and maximum cycle numbers were tested on the perfor-mance of ABC under the conditions of no scout bee,single scout bee and multi-scout bees conditions. Al-most all the experimental results show that ABC with multi-scout bees outperforms ABC with single scout bee and ABC without scout bee on five well-known benchmark functions,meanwhile,ABC with single scout bee performs better than ABC without scout bee. Besides,the three algorithms have almost the same execution time under the same conditions due to the same order of their search complexity. These fully demonstrate that the random exploration process adopted by scout bees has positive effect on the performance of ABC.国家自然科学基金资助项目(70971020);广西混杂计算与集成电路设计分析重点实验室开放基金资助项目(2012HCI09);广西民族大学重点科研资助项目(2012MDZD035

Combine multiple ensemble classifiers for medical diagnosis

在计算机辅助诊断系统中使用集成分类器是提高机器识别能力的一种重要途径。针对集成分类器投票组合算法中存在的投票可信度问题,提出了一种基于Grading的集成分类器组合算法EGR,该算法根据集成分类器对样本的预测结果是否正确来转换相应样本的类标签,用新数据构造元分类器。在UCI医学数据集上进行的实验结果显示,EGR算法对分类精度的提升以及敏感性与特异性的整体改善是有效的。Employing ensemble classifiers in the Computer-Aided Diagnosis system is an efficient way to improve recognition ability. Concerning the disadvantage of voting without considering confi- dence in method of combining multiple ensemble classifiers, a new combination algorithm EGR based on Grading was proposed, which transform original example class label according to the cor- rectness between true class and prediction output by ensemble classifiers, and meta classifiers are constructed on these new training data. Experiments on UCI machine learning medical datasets dem- onstrate the effectiveness of EGR in terms of better classification accuracy and improved sensitivity and specificity.国家自然科学基金资助项目(70971020);广西混杂计算与集成电路设计分析重点实验室2012年度开放课题(2012HCI09);广西民族大学科研资助项目(2011MDYB033、2012MDZD035