基于精简随机子空间的多生成树集成一类分类算法

由于高维数据通常存在冗余和噪声,在其上直接构造覆盖模型不能充分反映数据的分布信息,导致分类器性能下降.为此提出一种基于精简随机子空间多树集成分类方法.该方法首先生成多个随机子空间,并在每个子空间上构造独立的最小生成树覆盖模型.其次对每个子空间上构造的分类模型进行精简处理,通过一个评估准则(AUC值),对生成的一类分类器进行精简.最后均值合并融合这些分类器为一个集成分类器.实验结果表明,与其它直接覆盖分类模型和bagging算法相比,多树集成覆盖分类器具有更高的分类正确率.     

支持向量分类机的修正核函数

核函数是支持向量机的核心,它的作用主要体现在处理非线性问题时,将研究问题从低维空间转化成高维空间,使之在高维空间中变成线性问题,核函数的研究在支持向量机中是非常必要的。首先讨论核函数的本质,并且基于黎曼几何结构和数据依赖的方法,提出了一种改进的修正核函数,改进后的核函数形式简单,计算量较低,其中保形因子与支持向量无关,较之于以前的研究克服了支持向量的数目和分布的影响。将该核函数用于模式分类中,取得了良好的效果,显著提高了支持向量分类机的泛化能力。     

基于粗糙集和神经网络的分类器及其在LPR中的应用

粗糙集和神经网络在模式识别中都可用于分类,但是都有局限性。虽然两者没有太多的共同点,将它们结合起来却能相互补充,起到比单个理论更好的分类效果。本文从理论上给出了用粗糙集约简算法减少BP网络中的一个神经元或连接时网络输出能产生的最大误差。接着将粗糙集和BP网络结合起来设计分类器,并通过车牌数字识别验证了该分类器的有效性。实验说明该分类器比单独用粗糙集和神经网络设计的分类器识别率高、识别时间短。     

基于支持向量机的导航星选取算法研究

在星敏感器导航星表的建立过程中由于恒星的数量太多, 往往要进行筛选, 通常这种选择是一种基于枚举的大量反复的提取过程, 复杂费时而结果往往并不是最优的。而基于统计学习理论( SLT) 的支持向量机( SVM) 方法正好克服了这方面的不足。SLT 理论和SVM 方法为导航星选取过程的简化和结果的最优性的获得提供了新的途径。讨论了支持向量机在导航星选取优化中进行应用的分类算法, 构建了导航星分类器, 并以导航星的选取为例进行了试验论证。试验表明: 基于SVM 的导航星分类器对简化导航星的筛选过程优化导航星表的     

基于证据组合的通信信号调制类型的分类识别

该文应用Dempster-Shafer理论,提出了一种有效的证据分配和组合证据最佳区间的方法,来实现通信信号调制类型的分类识别,在低信噪比的情况下,识别率得到较大提高,并且降低了分类器设计的复杂性。计算机模拟结果证实了此方法的有效性。     

一种基于粒子群算法的分类器设计

将粒子群算法应用于数据分类,给出了适用于粒子群算法的分类规则编码,构造了新的分类规则适应度函数来更准确的提取规则集,并通过修改粒子位置更新方程使粒子群算法适于解决分类规则挖掘问题,进而实现了基于粒子群算法的分类器设计。该文进一步用UCI基准数据集对作者提出的粒子群分类器进行了测试,并将几种不同速度与位置更新策略的粒子群算法分类器与遗传算法分类器进行对比,实验结果表明,这种粒子群分类器是一种有效、可行的分类器设计方案。     

Learning control of autonomous robots using an instance-based classifier generator in continuous state space

A classifier system for the reinforcement learning control of autonomous mobile robots is proposed. The classifier system contains action selection, rules reproduction, and credit assignment mechanisms. An important feature of the classifier system is that it operates with continuous sensor and action spaces. The system is applied to the control of mobile robots. The local controllers use independent classifiers specified at the wheel-level. The controllers work autonomously, and with respect to each other represent dynamic systems connected through the external environment. The feasibility of the proposed system is tested in an experiment with a Khepera robot. It is shown that some patterns of global behavior can emerge from locally organized classifiers. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

Generalized fuzzy Petri nets as pattern classifiers

In this study, we discuss a novel approach to pattern classification using a concept of fuzzy Petri nets. In contrast to the commonly encountered Petri nets with their inherently Boolean character of processing tokens and firing transitions, the proposed generalization involves continuous variables. This extension makes the nets to be fully in rapport with the panoply of the real-world classification problems. The introduced model of the fuzzy Petri net hinges on the logic nature of the operations governing its underlying behavior. The logic-driven effect in these nets becomes especially apparent when we are concerned with the modeling of its transitions and expressing pertinent mechanisms of a continuous rather than an on–off firing phenomenon. An interpretation of fuzzy Petri nets in the setting of pattern classification is provided. This interpretation helps us gain a better insight into the mechanisms of the overall classification process. Input places correspond to the features of the patterns. Transitions build aggregates of the generic features giving rise to their logical summarization. The output places map themselves onto the classes of the patterns while the marking of the places correspond to the class of membership values. Details of the learning algorithm are also provided along with an illustrative numeric experiment.     

Sentiment analysis of extremism in social media from textual information

Uncertainty in political, religious, and social issues causes extremism among people that are depicted by their sentiments on social media. Although, English is the most common language used to share views on social media, however, other vicinity based languages are also used by locals. Thus, it is also required to incorporate the views in such languages along with widely used languages for revealing better insights from data. This research focuses on the sentimental analysis of social media multilingual textual data to discover the intensity of the sentiments of extremism. Our study classifies the incorporated textual views into any of four categories, including high extreme, low extreme, moderate, and neutral, based on their level of extremism. Initially, a multilingual lexicon with the intensity weights is created. This lexicon is validated from domain experts and it attains 88% accuracy for validation. Subsequently, Multinomial Naïve Bayes and Linear Support Vector Classifier algorithms are employed for classification purposes. Overall, on the underlying multilingual dataset, Linear Support Vector Classifier out-performs with an accuracy of 82%.     

An investigation into the effects of label noise on Dynamic Selection algorithms

In the literature on classification problems, it is widely discussed how the presence of label noise can bring about severe degradation in performance. Several works have applied Prototype Selection techniques, Ensemble Methods, or both, in an attempt to alleviate this issue. Nevertheless, these methods are not always able to sufficiently counteract the effects of noise. In this work, we investigate the effects of noise on a particular class of Ensemble Methods, that of Dynamic Selection algorithms, and we are especially interested in the behavior of the Fire-DES++ algorithm, a state of the art algorithm which applies the Edited Nearest Neighbors (ENN) algorithm to deal with the effects of noise and imbalance. We propose a method which employs multiple Dynamic Selection sets, based on the Bagging-IH algorithm, which we dub Multiple-Set Dynamic Selection (MSDS), in an attempt to supplant the ENN algorithm on the filtering step. We find that almost all methods based on Dynamic Selection are severely affected by the presence of label noise, with the exception of the K-Nearest Oracles-Union algorithm. We also find that our proposed method can alleviate the issues caused by noise in some scenarios. We have made the code for our method available at https://github.com/fnw/baggingds.