基于Naive Bayes的维吾尔文文本分类算法及其性能分析

以大规模网络维吾尔文文本的自动分类技术研究为背景,设计模块化结构的维吾尔文本分类系统,在深入调研基础上选择Naive Bayes算法为分类引擎,用C#实现分类系统。预处理中,结合维吾尔语的词法特征,通过引入词干提取方法大大降低特征维数。在包含10大类共计3 000多个较大规模文本语料库基础上给出分类实验结果,再通过x2统计方法选择不同数目的特征,也分别给出分类实验结果。结果表明,预处理后的维吾尔文特征空间中只有1%-3%特征是最佳的,因而进一步确定哪些是最佳特征或降低特征空间维数是有可能的。     

基于交叉覆盖算法的文本分类

分类是文本信息搜索和挖掘的核心内容,被广泛应用于搜索引擎的设计以及数据挖掘的研究中。首先对文本进行分词,对分词的结果采用x2统计量的方法提取特征,再使用前向神经网络的交叉覆盖算法作为分类器进行文本分类。实验表明,x2统计量可大规模降低特征维数,在此基础上结合交叉覆盖算法的优秀分类能力,可在特征维数较低的情况下获得一个性能较好的文本分类器。     

基于交叉覆盖算法的文本分类

分类是文本信息搜索和挖掘的核心内容,被广泛应用于搜索引擎的设计以及数据挖掘的研究中。首先对文本进行分词,对分词的结果采用x2统计量的方法提取特征,再使用前向神经网络的交叉覆盖算法作为分类器进行文本分类。实验表明,x2统计量可大规模降低特征维数,在此基础上结合交叉覆盖算法的优秀分类能力,可在特征维数较低的情况下获得一个性能较好的文本分类器。     

组合降维技术在中文网页分类中的应用

基于向量空间模型的文本分类中特征向量是极度稀疏的高维向量,只有降低向量空间维数才能提高分类效率。在利用统计方法选择文本分类特征降低特征空间维数的基础上,采用隐含语义分析技术,挖掘文档特征间的语义信息,利用矩阵奇异值分解理论进一步降低了特征空间维数。实验结果表明分类结果宏平均F1约提高了5%,验证了该方法的有效性。     

基于聚类分析的图模型文档分类

针对传统向量空间模型中的特征项孤立处理问题,首先通过χ2统计和特征聚类相结合的模式实现特征降维,然后使用图模型来建立词和词之间相互关联信息,最后运用KNN方法进行文档分类测试。该算法提高了稀有词对分类的贡献,强化了关联词的分类效果,并降低了文档向量的维数。实验证明,该算法提高了分类的准确率和召回率。     

基于多分类SVM-KNN的实体关系抽取方法

实体关系抽取是信息抽取领域的重要研究课题之一。传统的实体关系抽取研究注重于从实体对出现的上下文中提取词法和语义等特征,然后利用分类器(如SVM)进行实体关系抽取,但该类方法忽略了分类器对实体抽取性能的影响。针对SVM分类器对超平面附近样本分类正确率低的问题,本文设计了一种基于双投票机制的SVM模糊样本选择方法。在此基础上,对确定区域样本直接使用SVM分类器进行分类,并利用KNN算法对模糊区域样本进行二次分类。在SemEval-2010评测任务提供的实体关系抽取数据上进行实验,实验结果表明该方法能较大提高实体关系抽取的性能。     

基于粗糙集和灰色关联度的综合性特征选择

在文本特征空间中,特征维数通常高达几万,这大大限制了分类算法的选择,降低了分类算法的性能,影响了分类器的设计,为此需要进行特征选择以避免“维数灾难”。提出了一个综合性的特征选择方法,该方法首先利用一个优化的文档频进行特征初选以过滤掉一些词条来降低特征空间的稀疏性,然后利用一个基于粗糙集和灰色关联度的属性约简算法来消除冗余,从而获得较具代表性的特征子集。实验结果表明该综合性方法效果良好。     

文本特征选择算法MI的改进

特征选择在文本挖掘技术中是一个关键部分.训练集中的文本逐个经过分词后,可形成文本分类系统的全特征空间,一般情况下,这个空间的维数都会较大,可达到几十万维.经过特征选择之后,在降低噪声的同时,特征空间的维数得以压缩,最终能提高分类算法的速度和分类精度.本文从传统的Ml(Mutual Infomation)出发,并对它进行...     

基于模拟退火及蜂群算法的优化特征选择算法

为了解决中文文本分类中初始特征空间维数过高带来的“维数灾难”问题,提高分类精度和分类效率,提出了一种基于模拟退火及蜂群算法的优化特征选择算法.该算法中,以蜂群算法流程为主体,根据蜜蜂群体觅食的特点快速寻找最优解,并且针对蜂群算法容易陷入局部最优解的问题,把模拟退火算法机制引入其中.该算法既保留了蜂群算法群体寻优的特点,又可以有效地避免陷入局部最优解.通过选择合适的收益率函数和温度下降函数,用实验的方法与卡方统计、信息增益和互信息等算法进行比较,表明了该算法的可行性和有效性.     

基于统计方法的面向旅游问句分类实验研究

根据旅游领域知识的特点,设计了面向旅游问句的分类体系。利用信息增益、互信息、交叉熵和X^2统计四种特征选择方法及支持向量机分类器,对网上常见的旅游真实问句分类进行了实验研究,实验结果表明：在现有问句分类体系下,信息增益的特征选择方法在特征空间维数为550维时,分类旅游问句的结果是最佳的。     

Geometrically local embedding in manifolds for dimension reduction

In this paper, geometrically local embedding (GLE) is presented to discover the intrinsic structure of manifolds as a method in nonlinear dimension reduction. GLE is able to reveal the inner features of the input data in the lower dimension space while suppressing the influence of outliers in the local linear manifold. In addition to feature extraction and representation, GLE behaves as a clustering and classification method by projecting the feature data into low-dimensional separable regions. Through empirical evaluation, the performance of GLE is demonstrated by the visualization of synthetic data in lower dimension, and the comparison with other dimension reduction algorithms with the same data and configuration. Experiments on both pure and noisy data prove the effectiveness of GLE in dimension reduction, feature extraction, data visualization as well as clustering and classification.     

A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases

In this paper, a new feature selection method based on Association Rules (AR) and Neural Network (NN) is presented for the diagnosis of erythemato-squamous diseases. AR is used for reducing the dimension of erythemato-squamous diseases dataset and NN is used for efficient classification. The proposed AR+NN system performance is compared with that of other feature selection algorithms+NN. The dimension of input feature space is reduced from thirty four to twenty four by using AR. In test stage, 3-fold cross validation method is applied to the erythemato-squamous diseases dataset to evaluate the proposed system performances. The correct classification rate of proposed system is 98.61%. This research demonstrated that the AR can be used for reducing the dimension of feature space and proposed AR+NN model can be used to obtain fast automatic diagnostic systems for other diseases.     

提高小样本高光谱图像分类性能的变维卷积神经网络

目的 为了解决基于卷积神经网络的算法对高光谱图像小样本分类精度较低、模型结构复杂和计算量大的问题,提出了一种变维卷积神经网络。方法 变维卷积神经网络对高光谱分类过程可根据内部特征图维度的变化分为空—谱信息融合、降维、混合特征提取与空—谱联合分类的过程。这种变维结构通过改变特征映射的维度,简化了网络结构并减少了计算量,并通过对空—谱信息的充分提取提高了卷积神经网络对小样本高光谱图像分类的精度。结果 实验分为变维卷积神经网络的性能分析实验与分类性能对比实验,所用的数据集为Indian Pines和Pavia University Scene数据集。通过实验可知,变维卷积神经网络对高光谱小样本可取得较高的分类精度,在Indian Pines和Pavia University Scene数据集上的总体分类精度分别为87.87%和98.18%,与其他分类算法对比有较明显的性能优势。结论 实验结果表明,合理的参数优化可有效提高变维卷积神经网络的分类精度,这种变维模型可较大程度提高对高光谱图像中小样本数据的分类性能,并可进一步推广到其他与高光谱图像相关的深度学习分类模型中。     

Object detection using feature subset selection

Past work on object detection has emphasized the issues of feature extraction and classification, however, relatively less attention has been given to the critical issue of feature selection. The main trend in feature extraction has been representing the data in a lower dimensional space, for example, using principal component analysis (PCA). Without using an effective scheme to select an appropriate set of features in this space, however, these methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper, we argue that feature selection is an important problem in object detection and demonstrate that genetic algorithms (GAs) provide a simple, general, and powerful framework for selecting good subsets of features, leading to improved detection rates. As a case study, we have considered PCA for feature extraction and support vector machines (SVMs) for classification. The goal is searching the PCA space using GAs to select a subset of eigenvectors encoding important information about the target concept of interest. This is in contrast to traditional methods selecting some percentage of the top eigenvectors to represent the target concept, independently of the classification task. We have tested the proposed framework on two challenging applications: vehicle detection and face detection. Our experimental results illustrate significant performance improvements in both cases.     

基于免疫文化算法的故障特征选择方法

提出了一种新的文化算法,基于免疫克隆选择原理改进了文化算法的种群空间,同时设计了一种新的历史知识及其影响函数。为了去除工业中故障诊断过程中的冗余变量,实现数据降维,提高故障诊断性能,将该免疫文化算法应用到故障特征选择当中,提出了一种封装式的特征选择方法。该方法利用抗体种群进行全局搜索,通过文化算法的信念空间保留历代最优个体,并对UCI数据集的高维数据进行特征子集选择。将该方法应用到TE过程故障诊断中,结果表明,相比于直接使用高维数据进行故障诊断,该算法有效降低了特征空间的维数,提高了分类精度。     

A random forest classifier for lymph diseases

Machine learning-based classification techniques provide support for the decision-making process in many areas of health care, including diagnosis, prognosis, screening, etc. Feature selection (FS) is expected to improve classification performance, particularly in situations characterized by the high data dimensionality problem caused by relatively few training examples compared to a large number of measured features. In this paper, a random forest classifier (RFC) approach is proposed to diagnose lymph diseases. Focusing on feature selection, the first stage of the proposed system aims at constructing diverse feature selection algorithms such as genetic algorithm (GA), Principal Component Analysis (PCA), Relief-F, Fisher, Sequential Forward Floating Search (SFFS) and the Sequential Backward Floating Search (SBFS) for reducing the dimension of lymph diseases dataset. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the RFC for efficient classification. It was observed that GA-RFC achieved the highest classification accuracy of 92.2%. The dimension of input feature space is reduced from eighteen to six features by using GA.     

Feature extraction based on ICA for binary classification problems

In manipulating data such as in supervised learning, we often extract new features from the original features for the purpose of reducing the dimensions of feature space and achieving better performance. In this paper, we show how standard algorithms for independent component analysis (ICA) can be appended with binary class labels to produce a number of features that do not carry information about the class labels-these features will be discarded-and a number of features that do. We also provide a local stability analysis of the proposed algorithm. The advantage is that general ICA algorithms become available to a task of feature extraction for classification problems by maximizing the joint mutual information between class labels and new features, although only for two-class problems. Using the new features, we can greatly reduce the dimension of feature space without degrading the performance of classifying systems.     

一种改进的动态流特征选择算法

流特征选择算法在深度流检测技术中发挥着重要作用,数据流的正确识别和分类都需要选择流特征,通过这些流特征在业务流中的差异区分业务流类型。当前基于信息度量的特征选择算法在整个样本空间中计算特征的信息熵,没有将特征选择过程中的动态变化信息加入计算,因此不能准确地度量特征选择过程中各个特征之间的相互关系程度,冗余信息的存在影响特征选择结果,导致分类算法性能降低。提出一种改进的动态特征选择算法,该算法基于信息标准,充分考虑特征选择过程中信息标准的动态变化,通过删除由信息动态变化导致的冗余及无用信息,避免动态选择过程的干扰,达到准确并高效选择特征的目的。实验数据说明,提出的动态流特征选择算法的分类性能比当前其他选择算法较好。     

Feature selection for modular GA-based classification

Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. Feature selection plays an important role in finding relevant features in classification. In this paper, feature selection is explored with modular GA-based classification. A new feature selection technique, relative importance factor (RIF), is proposed to find less relevant features in the input domain of each class module. By removing these features, it is aimed to reduce the classification error and dimensionality of classification problems. Benchmark classification data sets are used to evaluate the proposed approach. The experiment results show that RIF can be used to find less relevant features and help achieve lower classification error with the feature space dimension reduced.     

一种基于属性关系的特征选择算法

对于包含大量特征的数据集, 特征选择已成为一个研究热点, 能剔除无关和冗余特征, 将会有效改善分类准确性. 对此, 在分析已有文献的基础上, 提出一种基于属性关系的特征选择算法(NCMIPV), 获取优化特征子集, 并在UCI 数据集上对NCMIPV 算法进行性能评估. 实验结果表明, 与原始特征子集相比, 该算法能有效降低特征空间维数, 运行时间也相对较短, 分类差错率可与其他算法相比, 在某些场合下性能明显优于其他算法.