一种分类器选择方法

在按照“测试-选择”方法设计多分类器系统时,从超量生成的候选分类器集中选取一个最优子集是关键环节之一。基于此,定义一个组合适宜度概念,提出一种新的分类器选择方法。将该方法用于高光谱遥感数据分类实验中,并从具有27个候选的分类器集中挑选子集。实验结果表明,该方法在选择效率和识别精度方面具有优势,能保证所选子集的泛化能力。     

基于免疫克隆的核匹配追踪集成图像识别算法

为了从分类器集成系统中选择一组较优个体子集,从而改善集成学习系统的性能,提出一种基于免疫克隆选择的核匹配追踪集成图像识别算法.该算法充分利用免疫克隆算法的快速收敛于全局最优解的特性,对训练得到的多个子核匹配追踪分类器进行免疫克降选择,得到一个具有更好推广性能的集成系统.对Brodatz纹理图像库以及SAR图像进行目标识别.仿真实验结果表明,相比传统核匹配追踪分类器集成和基于遗传算法的选择集成方法,本文方法有更好的集成性能.     

一种新的分类器选择集成算法

为提高集成分类器系统的分类性能,提出一种分类器选择集成算法MCC-SCEN。该算法选取基分类器集中具有最大互信息差异性的子集和最大个体分类能力的子集,以确定待扩展分类器集,选择具有较大混合分类能力的基分类器加入到待扩展集中,构成集成系统,进行加权投票并产生结果。实验结果表明,该方法优于经典的AdaBoost和Bagging方法,具有较高的分类准确率。     

基于遗传禁忌搜索的分类器选择集成方法

为提高入侵检测的精度,提出一种使用遗传禁忌搜索的分类器选择集成方法。该方法采用Bagging算法构建初始分类器集合,根据遗传禁忌搜索算法选择分类器子集,以该子集建立多分类器系统进行入侵检测。实验结果表明,与Bagging算法相比,该方法能有效提高检测精度、降低误报率。     

用于特征子集选择的异步并行微粒群优化方法

针对大量无关或冗余的特征通常会降低模式分类中分类器性能的问题,提出一种基于异步并行微粒群优化的特征子集选择方法（AP-PSO）.该方法采用二进制微粒群优化搜索特征子集,利用异步并行方式提高算法的运算效率;为有效协调种群的全局探索和局部开发能力,充分利用混沌运动的遍历性和随机性,提出一种一致混沌变异算子.与已知4种特征子集选择方法进行比较,所得结果验证了该算法的有效性.     

基于重采样和集成选择的适用于实体识别的多分类器系统

实体识别常利用分类器根据记录对的字段相似度向量将记录对分为匹配、不匹配和可能匹配,因此分类器的准确性与实体识别的准确性直接相关。为提高分类准确性,本文基于重采样和集成选择技术构建一个多分类器系统。充分利用实体识别的特点,在分类之前发现分类困难的样本,并使重采样比率在一个区间内变化,生成一组重采样样本;然后用重采样后的样本训练分类器构建一个并行多分类器系统,强调分类器之间的差异度和稀疏度,从该多分类器系统中选择最优分类器子集,即最优的重采样比率组合,分别用非线性规划和极值方法求解该集成选择模型。实验结果表明,本方法与现有的多分类器系统相比具有更高的准确性。     

面向高维微阵列数据的集成特征选择算法

特征选择算法是微阵列数据分析的重要工具,特征选择算法的分类性能和稳定性对微阵列数据分析至关重要。为了提高特征选择算法的分类性能和稳定性,提出一种面向高维微阵列数据的集成特征选择算法来弥补单个基因子集信息量的不足,提高基因特征选择算法的分类性能和稳定性。该算法首先采用信噪比方法选择若干区分基因;然后对每个区分基因利用条件信息相关系数评估候选基因与区分基因的相关性,生成多个相关基因子集,最后,通过集成学习技术整合多个相似基因子集。实验结果表明,本文提出的集成特征选择算法的分类性能以及稳定性在多数情况下均优于只选择单个基因子集的方法。     

分类器的动态选择与循环集成方法

针对多分类器系统设计中最优子集选择效率低下、集成方法缺乏灵活性等问题, 提出了分类器的动态选择与循环集成方法 (Dynamic selection and circulating combination, DSCC). 该方法利用不同分类器模型之间的互补性, 动态选择出对目标有较高识别率的分类器组合, 使参与集成的分类器数量能够随识别目标的复杂程度而自适应地变化, 并根据可信度实现系统的循环集成. 在手写体数字识别实验中, 与其他常用的分类器选择方法相比, 所提出的方法灵活高效, 识别率更高.     

基于遗传算法和支持向量机的肿瘤分子分类

提出了一种基于遗传算法(GA)和支持向量机(SVM)的用于肿瘤分子分类和特征基因选择的新方法。该方法针对基因表达数据样本少维数高的特点,先根据基因的散乱度滤掉大量分类无关基因,而后使用相关性分析去除分类冗余基因,得到一个候选基因子集,用遗传算法搜索候选特征基因空间,发现在支持向量机分类器上具有好的分类性能的且含基因个数较少的特征子集。把这种GA/SVM方法应用到结肠癌和急性白血病基因表达谱,能选出多个取得较高分类精度的较小基因子集,实验结果表明了该方法的有效性。     

数据流分类中的增量特征选择算法

概念流动的出现及数据的高维性增加了数据流特征选择的复杂性。信息增益是最有效的特征选择算法之一,但计算量大。对信息增益做了等价替换,提出一种基于改进信息增益的混合增量特征选择(IFS)算法。该算法首先利用与分类器无关的评价函数选出候选特征集合,然后将分类器作用于候选特征集合,利用分类精度作为评价标准去选择特征子集,在遇到概念漂移时重新选择特征子集。通过在超平面数据集和UCI数据集上的实验,表明基于IFS算法的分类器能够很快地适应概念漂移,并且比基于全部特征的分类算法有更高的精度。     

动态朴素贝叶斯网络分类器的特征子集选择

分类准确性是分类器最重要的性能指标,特征子集选择是提高分类器分类准确性的一种有效方法。现有的特征子集选择方法主要针对静态分类器,缺少动态分类器特征子集选择方面的研究。首先给出具有连续属性的动态朴素贝叶斯网络分类器和动态分类准确性评价标准,在此基础上建立动态朴素贝叶斯网络分类器的特征子集选择方法,并使用真实宏观经济时序数据进行实验与分析。     

根据混合选择策略的直觉模糊核匹配追踪集成算法

为了从分类器集成系统中选择出一组差异性大的子分类器,从而提高集成系统的泛化能力,提出了一种基于混合选择策略的直觉模糊核匹配追踪算法.基本思想是通过扰动训练集和特征空间生成一组子分类器;然后采用k均值聚类算法将对所得子分类器进行修剪,删去其中的冗余分类器;最后根据实际识别目标动态选择出较高识别率的分类器组合,使选择性集成规模能够随识别目标的复杂程度而自适应地变化,并基于预期识别精度实现循环集成.实验结果表明,与其他常用的分类器选择方法相比,本文方法灵活高效,具有更好的识别效果和泛化能力.     

Feature selection using mutual information in CT colonography

Computed tomographic (CT) colonography is a promising alternative to traditional invasive colonoscopic methods used in the detection and removal of cancerous growths, or polyps in the colon. Existing computer-aided diagnosis (CAD) algorithms used in CT colonography typically employ the use of a classifier to discriminate between true and false positives generated by a polyp candidate detection system based on a set of features extracted from the candidates. However, these classifiers often suffer from a phenomenon termed the curse of dimensionality, whereby there is a marked degradation in the performance of a classifier as the number of features used in the classifier is increased. In addition, an increase in the number of features used also contributes to an increase in computational complexity and demands on storage space.This paper investigates the benefits of feature selection on a polyp candidate database, with the aim of increasing specificity while preserving sensitivity. Two new mutual information methods for feature selection are proposed in order to select a subset of features for optimum performance. Initial results show that the performance of the widely used support vector machine (SVM) classifier is indeed better with the use of a small set of features, with receiver operating characteristic curve (AUC) measures reaching 0.78-0.88.     

A dynamic overproduce-and-choose strategy for the selection of classifier ensembles

The overproduce-and-choose strategy, which is divided into the overproduction and selection phases, has traditionally focused on finding the most accurate subset of classifiers at the selection phase, and using it to predict the class of all the samples in the test data set. It is therefore, a static classifier ensemble selection strategy. In this paper, we propose a dynamic overproduce-and-choose strategy which combines optimization and dynamic selection in a two-level selection phase to allow the selection of the most confident subset of classifiers to label each test sample individually. The optimization level is intended to generate a population of highly accurate candidate classifier ensembles, while the dynamic selection level applies measures of confidence to reveal the candidate ensemble with the highest degree of confidence in the current decision. Experimental results conducted to compare the proposed method to a static overproduce-and-choose strategy and a classical dynamic classifier selection approach demonstrate that our method outperforms both these selection-based methods, and is also more efficient in terms of performance than combining the decisions of all classifiers in the initial pool.     

Fuzzy classifier design using harmonic search methods

A new approach to design of a fuzzy-rule-based classifier that is capable of selecting informative features is discussed. Three basic stages of the classifier construction—feature selection, generation of fuzzy rule base, and optimization of the parameters of rule antecedents—are identified. At the first stage, several feature subsets on the basis of discrete harmonic search are generated by using the wrapper scheme. The classifier structure is formed by the rule base generation algorithm by using extreme feature values. The optimal parameters of the fuzzy classifier are extracted from the training data using continuous harmonic search. Akaike information criterion is deployed to identify the best performing classifiers. The performance of the classifier was tested on real-world KEEL and KDD Cup 1999 datasets. The proposed algorithms were compared with other fuzzy classifiers tested on the same datasets. Experimental results show efficiency of the proposed approach and demonstrate that highly accurate classifiers can be constructed by using relatively few features.     

A selection framework of sensor combination feature subset for human motion phase segmentation

Motion phase plays an important role in the spatial–temporal parameters of human motion analysis. Multi-sensor fusion technology based on inertial sensors frees the monitoring of the human body phase from space constraints and improves the flexibility of the system. However, human phase segmentation methods usually rely on the determination of the positioning of the sensor and the number of sensors, it is difficult to artificially select the number and position of the sensors, especially when human motion phases are diverse. This paper proposes a selection framework for the sensor combination feature subset for motion phase segmentation, which combines feature selection algorithms with the subsequent classifiers, and determine the optimum combination of the sensor and the feature subset according to the performance of the trained model. Through the constraint and the sensor combination feature subset (SCFS), the filter method can select any number of sensors and control the size of the feature subset; the embedded method can select any number of sensors, but the size of the feature subset is determined by the classifier model. Experimental results show that the proposed framework can effectively select a specified number of sensors without human intervention, and the number of sensors has an impact on the recognition rate of the classifier within 1.5%. In addition, the filter method has good adaptability to a variety of classifiers, and the classifier prediction time can be controlled by setting the subset size of the feature; the embedded method can achieve a better phase segmentation effect than the filter method. For the application of motion phase segmentation, the proposed framework can reliably and quickly identify redundant sensors that provide effective support for reducing the complexity of the wearable sensor system and improving user comfort.     

A measure of competence based on random classification for dynamic ensemble selection

In this paper, a measure of competence based on random classification (MCR) for classifier ensembles is presented. The measure selects dynamically (i.e. for each test example) a subset of classifiers from the ensemble that perform better than a random classifier. Therefore, weak (incompetent) classifiers that would adversely affect the performance of a classification system are eliminated. When all classifiers in the ensemble are evaluated as incompetent, the classification accuracy of the system can be increased by using the random classifier instead. Theoretical justification for using the measure with the majority voting rule is given. Two MCR based systems were developed and their performance was compared against six multiple classifier systems using data sets taken from the UCI Machine Learning Repository and Ludmila Kuncheva Collection. The systems developed had typically the highest classification accuracies regardless of the ensemble type used (homogeneous or heterogeneous).