基于近似Markov Blanket和动态互信息的特征选择算法

针对大量无关和冗余特征的存在可能降低分类器性能的问题,提出了一种基于近似Markov Blanket和动态互信息的特征选择算法。该算法利用互信息作为特征相关性的度量准则,并在未识别的样本上对互信息进行动态估值,利用近似Markov Blanket原理准确地去除冗余特征,从而获得远小于原始特征规模的特征子集。通过仿真试验证明了该算法的有效性。以支持向量机为分类器,在公共数据集UCI上进行了试验,并与DMIFS和ReliefF算法进行了对比。试验结果证明,该算法选取的特征子集与原始特征子集相比,以远小于原始特征规模的特征子集获得了高于或接近于原始特征集合的分类结果。     

A NEW SUPERVISED FEATURE SELECTION METHOD FOR PATTERN CLASSIFICATION

With the rapid development of information techniques, the dimensionality of data in many application domains, such as text categorization and bioinformatics, is getting higher and higher. The high‐dimensionality data may bring many adverse situations, such as overfitting, poor performance, and low efficiency, to traditional learning algorithms in pattern classification. Feature selection aims at reducing the dimensionality of data and providing discriminative features for pattern learning algorithms. Due to its effectiveness, feature selection is now gaining increasing attentions from a variety of disciplines and currently many efforts have been attempted in this field. In this paper, we propose a new supervised feature selection method to pick important features by using information criteria. Unlike other selection methods, the main characteristic of our method is that it not only takes both maximal relevance to the class labels and minimal redundancy to the selected features into account, but also works like feature clustering in an agglomerative way. To measure the relevance and redundancy of feature exactly, two different information criteria, i.e., mutual information and coefficient of relevance, have been adopted in our method. The performance evaluations on 12 benchmark data sets show that the proposed method can achieve better performance than other popular feature selection methods in most cases.     

Measuring relevance between discrete and continuous features based on neighborhood mutual information

Measures of relevance between features play an important role in classification and regression analysis. Mutual information has been proved an effective measure for decision tree construction and feature selection. However, there is a limitation in computing relevance between numerical features with mutual information due to problems of estimating probability density functions in high-dimensional spaces. In this work, we generalize Shannon’s information entropy to neighborhood information entropy and propose a measure of neighborhood mutual information. It is shown that the new measure is a natural extension of classical mutual information which reduces to the classical one if features are discrete; thus the new measure can also be used to compute the relevance between discrete variables. In addition, the new measure introduces a parameter delta to control the granularity in analyzing data. With numeric experiments, we show that neighborhood mutual information produces the nearly same outputs as mutual information. However, unlike mutual information, no discretization is required in computing relevance when used the proposed algorithm. We combine the proposed measure with four classes of evaluating strategies used for feature selection. Finally, the proposed algorithms are tested on several benchmark data sets. The results show that neighborhood mutual information based algorithms yield better performance than some classical ones.     

基于专家特征的条件互信息多标记特征选择算法

特征选择对于分类器的分类精度和泛化性能起重要作用。目前的多标记特征选择算法主要利用最大相关性最小冗余性准则在全部特征集中进行特征选择,没有考虑专家特征,因此多标记特征选择算法的运行时间较长、复杂度较高。实际上,在现实生活中专家依据几个或者多个关键特征就能够直接决定整体的预测方向。如果提取关注这些信息,必将减少特征选择的计算时间,甚至提升分类器性能。基于此,提出一种基于专家特征的条件互信息多标记特征选择算法。首先将专家特征与剩余的特征相联合,再利用条件互信息得出一个与标记集合相关性由强到弱的特征序列,最后通过划分子空间去除冗余性较大的特征。该算法在7个多标记数据集上进行了实验对比,结果表明该算法较其他特征选择算法有一定优势,统计假设检验与稳定性分析进一步证明了所提出算法的有效性和合理性。     

基于互信息的分类属性数据特征选择算法

提出了一种针对分类属性数据特征选择的新算法。通过给出一种能够直接评价分类属性数据特征选择的评价函数新定义,重新构造能实现分类属性数据信息量、条件互信息、特征之间依赖度定义的计算公式,并在此基础上,提出了一种基于互信息较大相关、较小冗余的特征选择（MRLR）算法。MRLR算法在特征选择时不仅考虑了特征与类标签之间的相关性,而且还考虑了特征之间的冗余性。大量的仿真实验表明,MRLR算法在针对分类属性数据的特征选择时,能获得冗余度小且更具代表性的特征子集,具有较好的高效性和稳定性。     

Input feature selection by mutual information based on Parzen window

Mutual information is a good indicator of relevance between variables, and have been used as a measure in several feature selection algorithms. However, calculating the mutual information is difficult, and the performance of a feature selection algorithm depends on the accuracy of the mutual information. In this paper, we propose a new method of calculating mutual information between input and class variables based on the Parzen window, and we apply this to a feature selection algorithm for classification problems.     

基于局部子空间的多标记特征选择算法*

在已有的特征选择算法中,常用策略是通过相关准则选择与标记集合相关性较强的特征,然而该策略不一定是最优选择,因为与标记集合相关性较弱的特征可能是决定某些类别标记的关键特征.基于这一假设,文中提出基于局部子空间的多标记特征选择算法.该算法首先利用特征与标记集合之间的互信息得到一个重要度由高到低的特征序列,然后将新的特征排序空间划分为几个局部子空间,并在每个子空间设置采样比例以选择冗余性较小的特征,最后融合各子空间的特征子集,得到一组合理的特征子集.在6个数据集和4个评价指标上的实验表明,文中算法优于一些通用的多标记特征选择算法.     

基于互信息最大化的特征选择算法及应用

该文以互信息最大化原则为指导,经过推导和分析后提出了一种基于信息论模型的新的特征选择算法,称之为基于互信息最大化的特征选择算法（MaxMI）。基本思想就是特征选择后,应当尽可能多地保留关于类别的信息。该算法与传统的信息增益、互信息和交叉熵在表达形式上具有一定的相似性,但是并不完全相同。从实验上验证了基于互信息最大化的特征选择算法优于其它三种算法。     

一种新的最大相关最小冗余特征选择算法

传统的基于特征选择的分类算法中,由于其采用的冗余度和相关度评价标准单一,从而使得此类算法应用范围受限。针对这个问题,本文提出一种新的最大相关最小冗余特征选择算法,该算法在度量特征之间冗余度的评价准则中引入了两种不同的评价准则;在度量特征与类别之间的相关度中引入了4种不同的评价准则,衍生出8种不同的特征选择算法,从而使得该算法应用范围增大。此外,由于传统的最大相关最小冗余特征选择算法不能根据用户实际需求的数据维度进行特征选择。所以,引入了指示向量 $\lambda $ 来刻画用户实际的数据维度需求,提出了一种新的目标函数来求解最优特征子集,利用支持向量机对4个UCI数据集的特征子集进行了实验,最后,利用分类正确率、成对单边T检验充分验证了该算法的有效性。     

基于最大互信息最大相关熵的特征选择方法

特征选择算法主要分为filter和wrapper两大类,并已提出基于不同理论的算法模型,但依然存在算法处理能力不强、子集分类精度不高等问题。基于模糊粗糙集的信息熵模型提出最大互信息最大相关熵标准,并根据该标准设计了一种新的特征选择方法,能同时处理离散数据、连续数据和模糊数据等混合信息。经UCI数据集试验,表明该算法与其他算法相比,具有较高的精度,且稳定性较高,是有效的。     

A novel feature selection method and its application

In this paper, a novel feature selection method based on rough sets and mutual information is proposed. The dependency of each feature guides the selection, and mutual information is employed to reduce the features which do not favor addition of dependency significantly. So the dependency of the subset found by our method reaches maximum with small number of features. Since our method evaluates both definitive relevance and uncertain relevance by a combined selection criterion of dependency and class-based distance metric, the feature subset is more relevant than other rough sets based methods. As a result, the subset is near optimal solution. In order to verify the contribution, eight different classification applications are employed. Our method is also employed on a real Alzheimer’s disease dataset, and finds a feature subset where classification accuracy arrives at 81.3 %. Those present results verify the contribution of our method.     

基于动态相关性的特征选择算法

特征选择是从原始数据集中去除无关的特征并选择良好的特征子集,可以避免维数灾难和提高学习算法的性能。为解决已选特征和类别动态变化（DCSF）算法在特征选择过程中只考虑已选特征和类别之间动态变化的信息量,而忽略候选特征和已选特征的交互相关性的问题,提出了一种基于动态相关性的特征选择（DRFS）算法。该算法采用条件互信息度量已选特征和类别的条件相关性,并采用交互信息度量候选特征和已选特征发挥的协同作用,从而选择相关特征并且去除冗余特征以获得优良特征子集。仿真实验表明,与现有算法相比,所提算法能有效地提升特征选择的分类准确率。     

融合特征排序的多标记特征选择算法

在多标记学习框架中,特征选择是解决维数灾难,提高多标记分类器的有效手段。提出了一种融合特征排序的多标记特征选择算法。该算法首先在各标记下进行自适应的粒化样本,以此来构造特征与类别标记之间的邻域互信息。其次,对得到邻域互信息进行排序,使得每个类别标记下均能得到一组特征排序。最后,多个独立的特征排序经过聚类融合成一组新的特征排序。在4个多标记数据集和4个评价指标上的实验结果表明,所提算法优于一些当前流行的多标记降维方法。     

Feature selection, mutual information, and the classification of high-dimensional patterns

We propose a novel feature selection filter for supervised learning, which relies on the efficient estimation of the mutual information between a high-dimensional set of features and the classes. We bypass the estimation of the probability density function with the aid of the entropic-graphs approximation of Rényi entropy, and the subsequent approximation of the Shannon entropy. Thus, the complexity does not depend on the number of dimensions but on the number of patterns/samples, and the curse of dimensionality is circumvented. We show that it is then possible to outperform algorithms which individually rank features, as well as a greedy algorithm based on the maximal relevance and minimal redundancy criterion. We successfully test our method both in the contexts of image classification and microarray data classification. For most of the tested data sets, we obtain better classification results than those reported in the literature.     

A rough set approach to feature selection based on ant colony optimization

Rough set theory is one of the effective methods to feature selection, which can preserve the meaning of the features. The essence of rough set approach to feature selection is to find a subset of the original features. Since finding a minimal subset of the features is a NP-hard problem, it is necessary to investigate effective and efficient heuristic algorithms. Ant colony optimization (ACO) has been successfully applied to many difficult combinatorial problems like quadratic assignment, traveling salesman, scheduling, etc. It is particularly attractive for feature selection since there is no heuristic information that can guide search to the optimal minimal subset every time. However, ants can discover the best feature combinations as they traverse the graph. In this paper, we propose a new rough set approach to feature selection based on ACO, which adopts mutual information based feature significance as heuristic information. A novel feature selection algorithm is also given. Jensen and Shen proposed a ACO-based feature selection approach which starts from a random feature. Our approach starts from the feature core, which changes the complete graph to a smaller one. To verify the efficiency of our algorithm, experiments are carried out on some standard UCI datasets. The results demonstrate that our algorithm can provide efficient solution to find a minimal subset of the features.     

适合于入侵检测的分步特征选择算法

针对入侵检测数据集维数高,导致检测算法处理速度慢,而其中包含许多对检测效果影响不大的特征的问题,提出了一种分步特征选择算法。它通过对相关特征和冗余特征的定义,以互信息为准则,首先删除不相关特征,然后删除冗余特征。该算法的时间复杂性低,且独立于检测算法,可以通过调整阈值平衡检测精度和特征的数量。以权威数据集KDD-99为实验数据集,对多种检测算法进行了实验。结果表明,该算法能有效地选择特征向量,保证检测精度,提高检测速度。     

基于互信息的无监督特征选择

在数据分析中,特征选择可以用来降低特征的冗余,提高分析结果的可理解性和发现高维数据中隐藏的结构.提出了一种基于互信息的无监督的特征选择方法(UFS-MI),在UFS-MI中,使用了一种综合考虑了相关度和冗余度的特征选择标准UmRMR(无监督最小冗余最大相关)来评价特征的重要性.相关度和冗余度分别使用互信息来度量特征与潜在类别变量之间的依赖和特征与特征之间的依赖.UFS-MI同时适用于数值型和非数值型特征.在理论上证明了UFS-MI的有效性,实验结果也表明UFS-MI可以达到与传统的特征选择方法相当甚至更好的性能.     

基于最近邻互信息的特征选择算法

针对邻域信息系统的特征选择模型存在人为设定邻域参数值的问题。分别计算样本与最近同类样本和最近异类样本的距离,用于定义样本的最近邻以确定信息粒子的大小。将最近邻的概念扩展到信息理论,提出最近邻互信息。在此基础上,采用前向贪心搜索策略构造了基于最近邻互信息的特征算法。在两个不同基分类器和八个UCI数据集上进行实验。实验结果表明：相比当前多种流行算法,该模型能够以较少的特征获得较高的分类性能。     

基于标签关系改进的多标签特征选择算法

多标签特征选择是应对数据维度灾难现象的主要方法之一,可以在降低特征维度的同时提高学习效率,优化分类性能。针对目前特征选择算法没有考虑标签间的相互关系,以及信息量的衡量范围存在偏差的问题,提出一种基于标签关系改进的多标签特征选择算法。首先引入对称不确定性对信息量进行归一化处理,然后用归一化的互信息量作为相关性的衡量方法,并据此定义标签的重要性权重,对依赖度和冗余度中的标签相关项进行加权处理;进而提出一种特征评分函数作为特征重要性的评价指标,并依次选择出评分最高的特征组成最佳特征子集。实验结果表明,与其他算法相比,该算法在提取出更加精确的低维特征子集后,不仅能够有效提高面向实体信息挖掘的多标签学习算法的性能,也能提高基于离散特征的多标签学习算法的效率。     

混合Filter与改进自适应GA的特征选择方法

针对高维度小样本数据在特征选择时出现的维数灾难和过拟合的问题,提出一种混合Filter模式与Wrapper模式的特征选择方法（ReFS-AGA）。该方法结合ReliefF算法和归一化互信息,评估特征的相关性并快速筛选重要特征;采用改进的自适应遗传算法,引入最优策略平衡特征多样性,同时以最小化特征数和最大化分类精度为目标,选择特征数作为调节项设计新的评价函数,在迭代进化过程中高效获得最优特征子集。在基因表达数据上利用不同分类算法对简化后的特征子集分类识别,实验结果表明,该方法有效消除了不相关特征,提高了特征选择的效率,与ReliefF算法和二阶段特征选择算法mRMR-GA相比,在取得最小特征子集维度的同时平均分类准确率分别提高了11.18个百分点和4.04个百分点。