基于SU和AMB的网络流量特征选择算法

在基于网络流量分析,被动式的网络设备识别研究中,网络流量数据中往往存在许多高维数据,其中的部分特征对设备识别贡献不大,甚至会严重影响分类结果和分类性能.所以针对这个问题本文提出了一种将Filter和Wrapper方式相结合,基于对称不确定性(SU)和近似马尔可夫毯(AMB)的网络流量特征选择算法FSSA,本文提出的方法...     

Music classification via the bag-of-features approach

A central problem in music information retrieval is audio-based music classification. Current music classification systems follow a frame-based analysis model. A whole song is split into frames, where a feature vector is extracted from each local frame. Each song can then be represented by a set of feature vectors. How to utilize the feature set for global song-level classification is an important problem in music classification. Previous studies have used summary features and probability models which are either overly restrictive in modeling power or numerically too difficult to solve. In this paper, we investigate the bag-of-features approach for music classification which can effectively aggregate the local features for song-level feature representation. Moreover, we have extended the standard bag-of-features approach by proposing a multiple codebook model to exploit the randomness in the generation of codebooks. Experimental results for genre classification and artist identification on benchmark data sets show that the proposed classification system is highly competitive against the standard methods.     

Selection of Relevant Visual Feature Sets for Enhanced Depression Detection using Incremental Linear Discriminant Analysis

Presently, while automated depression diagnosis has made great progress, most of the recent works have focused on combining multiple modalities rather than strengthening a single one. In this research work, we present a unimodal framework for depression detection based on facial expressions and facial motion analysis. We investigate a wide set of visual features extracted from different facial regions. Due to high dimensionality of the obtained feature sets, identification of informative and discriminative features is a challenge. This paper suggests a hybrid dimensionality reduction approach which leverages the advantages of the filter and wrapper methods. First, we use a univariate filter method, Fisher Discriminant Ratio, to initially reduce the size of each feature set. Subsequently, we propose an Incremental Linear Discriminant Analysis (ILDA) approach to find an optimal combination of complementary and relevant feature sets. We compare the performance of the proposed ILDA with the batch-mode LDA and also the Composite Kernel based Support Vector Machine (CKSVM) method. The experiments conducted on the Distress Analysis Interview Corpus Wizard-of-Oz (DAIC-WOZ) dataset demonstrate that the best depression classification performance is obtained by using different feature extraction methods in combination rather than individually. ILDA generates better depression classification results in comparison to the CKSVM. Moreover, ILDA based wrapper feature selection incurs lower computational cost in comparison to the CKSVM and the batch-mode LDA methods. The proposed framework significantly improves the depression classification performance, with an F1 Score of 0.805, which is better than all the video based depression detection models suggested in literature, for the DAIC-WOZ dataset. Salient facial regions and well performing visual feature extraction methods are also identified.

     

Automatic hepatitis diagnosis system based on Linear Discriminant Analysis and Adaptive Network based on Fuzzy Inference System

In this paper, an automatic diagnosis system based on Linear Discriminant Analysis (LDA) and Adaptive Network based on Fuzzy Inference System (ANFIS) for hepatitis diseases is introduced. This automatic diagnosis system deals with the combination of feature extraction and classification. This automatic hepatitis diagnosis system has two stages, which feature extraction – reduction and classification stages. In the feature extraction – reduction stage, the hepatitis features were obtained from UCI Repository of Machine Learning Databases. Then, the number of these features was reduced to 8 from 19 by using Linear Discriminant Analysis (LDA). In the classification stage, these reduced features are given to inputs ANFIS classifier. The correct diagnosis performance of the LDA-ANFIS automatic diagnosis system for hepatitis disease is estimated by using classification accuracy, sensitivity and specificity analysis, respectively. The classification accuracy of this LDA-ANFIS automatic diagnosis system for the diagnosis of hepatitis disease was obtained in about 94.16%.     

谓词自动识别中的特征选择度量研究

谓词的自动识别是浅层句法分析的重要内容。本文提出了基于支持向量机分类算法的谓词自动识别方法,重点描述了在特征构建过程中基于信息增益的特征筛选方法与基于同义词词林的特征词度量方法。信息增益方法选取对分类影响较大的特征,降低了特征维度;同义词词林的度量方法将特征词映射为深层次的语义概念,增强了特征的表达能力,强调了属性特征与模型的相关度。在小规模语料库上的实验表明,谓词识别的最好F-Score达到了84.0%,相较于对数据无任何处理的情况F-Score提高了4.6%。结果表明,这种新的特征筛选与特征度量方法在谓词识别中十分有效,可以极大提高分类器的性能。     

融合内外特征的性别分类

针对以往仅用人脸特征或头发特征来进行性别分类的片面性,提出了将两类特征相融合的性别分类方法.用对光照、尺度变化具有很强鲁棒性的Gabor小波变换提取人脸内部特征并用PCA方法降维.利用最小代价原理,将动态搜索技术用于图像空间取得头发区域,定义了头发长度、头发表面积两种外部特征,并提出了相应的特征提取方法.采用模糊神经网络对三种特征进行非线性融合.在 Essex 人脸库中进行了性别分类实验,取得了97.1%的准确率.     

基于特征点类别可分性判断准则的图像分类

针对图像分类特征点特性界定模糊,导致相似性度量误差较大的问题,提出采用特征点类别可分性判断准则的图像分类方法。结合信息熵理论提取图像特征点的可分性特性,根据图像特征向量标识决策属性的不同性质,计算特征向量间的可分性距离值,得到最近邻特征向量集,从待分图像各特征向量与最近邻特征向量集标识类别的平均距离,及平均可分性度量值两方面定义新的图像类别判断准则。理论分析与Caltech256图像库仿真实验表明,基于特征点类别可分性判断准则有效地提高了图像的分类准确率。     

基于多层支持向量机的极化合成孔径雷达特征分析与分类

为了充分利用极化合成孔径雷达（SAR）图像不同极化特征对不同地物目标类型的刻画能力,提出一种基于多层支持向量机（SVM）的极化SAR特征分析与分类方法。该方法首先通过特征分析确定适合不同地物类型的最佳特征子集;然后采用分层分类树的方式,根据每一种地物类型的特征子集逐层进行SVM分类;最终得到整体分类结果。RadarSAT-2极化SAR图像分类实验结果表明所提方法水域、耕地、林地、城区4类地物分类精度为85%左右,总体分类精度达到86%。该算法充分利用了不同地物目标类型的特性,提高了分类精度,也降低了算法时间复杂度。     

分析参数相关和时序特征的飞行动作识别方法

利用飞行训练过程中记录的飞行参数数据,可以识别飞行员操作的飞行动作类型。飞行参数数据的各参数子序列具有时序性、相关性、各参数量纲不同、数据量大的特点,所以该方法考虑利用主成分分析提取其参数相关度统计特征,使用欧氏距离判别分析进行粗分类;然后提取相关度较大参数数据的时序趋势变化特征,利用动态时间弯曲距离匹配细分类,进一步识别动作区段归属的飞行动作类型。该方法能够在保证识别率的前提下,提高飞行动作识别的效率。     

云的光谱和纹理特征统计分析

利用静止卫星图像资料建立了夏季白天中低纬地区的11 种云/ 表面类型的样本集, 从中随机 挑选656 个样本, 提取116 个光谱和纹理特征参数并进行统计分析, 通过特征选择组成特征向量, 带入逐个修改聚类和模糊聚类的分类器进行敏感性试验。结果发现, 在反映云特征方面, 光谱特征 是云分类最基本的特征, 比纹理特征明显, 是云分类识别的主要依据; 除去水汽通道的标准差以外 其它光谱特征都比较明显, 红外和水汽通道的特征明显好于可见光通道, 尤其是对中低云和卷云的 描述。纹理特征在反映云特征方面也有一定的代表性, 特别是一阶概率特征中四通道的惯量及水汽 通道的逆差距; 纹理特征引入后分类准确率显著提高, 但在引入一阶概率特征基础上引入灰度级差 矢量特征效果改善并不明显。     

基于数据挖掘技术的加壳PE程序识别方法

恶意代码大量快速的繁衍使得恶意代码自动化检测成为必然趋势,加壳程序识别是恶意代码分析的一个必要步骤。为识别加壳可执行程序,提出一种基于数据挖掘技术的自动化加壳程序识别方法,该方法提取和选取可移植可执行(PE)特征,使用分类算法检测PE文件是否加壳。测试结果表明,在使用J48分类器时加壳文件识别率为98.7%。     

A comparative study on feature extraction for fingerprint classification and performance improvements using rank-level fusion

Fingerprint classification represents an important preprocessing step in fingerprint identification, which can be very helpful in reducing the cost of searching large fingerprint databases. Over the past years, several different approaches have been proposed for extracting distinguishable features and improving classification performance. In this paper, we present a comparative study involving four different feature extraction methods for fingerprint classification and propose a rank-based fusion scheme for improving classification performance. Specifically, we have compared two well-known feature extraction methods based on orientation maps (OMs) and Gabor filters with two new methods based on "minutiae maps" and "orientation collinearity". Each feature extraction method was compared with each other using the NIST-4 database in terms of accuracy and time. Moreover, we have investigated the issue of improving classification performance using rank-level fusion. When evaluating each feature extraction method individually, OMs performed the best. Gabor features fell behind OMs mainly because their computation is sensitive to errors in localizing the registration point. When fusing the rankings of different classifiers, we found that combinations involving OMs improve performance, demonstrating the importance of orientation information for classification purposes. Overall, the best classification results were obtained by fusing orientation map with orientation collinearity classifiers.     

Finger-vein pattern identification using SVM and neural network technique

This paper presents a support vector machine (SVM) technique for finger-vein pattern identification in a personal identification system. Finger-vein pattern identification is one of the most secure and convenient techniques for personal identification. In the proposed system, the finger-vein pattern is captured by infrared LED and a CCD camera because the vein pattern is not easily observed in visible light. The proposed verification system consists of image pre-processing and pattern classification. In the work, principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to the image pre-processing as dimension reduction and feature extraction. For pattern classification, this system used an SVM and adaptive neuro-fuzzy inference system (ANFIS). The PCA method is used to remove noise residing in the discarded dimensions and retain the main feature by LDA. The features are then used in pattern classification and identification. The accuracy of classification using SVM is 98% and only takes 0.015 s. The result shows a superior performance to the artificial neural network of ANFIS in the proposed system.     

Dimensionality reduction using genetic algorithms

Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern affect the success of subsequent classification. Feature extraction is the process of deriving new features from original features to reduce the cost of feature measurement, increase classifier efficiency, and allow higher accuracy. Many feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and classification efficiency, it does not necessarily reduce the number of features to be measured since each new feature may be a linear combination of all of the features in the original pattern vector. Here, we present a new approach to feature extraction in which feature selection and extraction and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a feature weight vector used to scale the individual features in the original pattern vectors. A masking vector is also employed for simultaneous selection of a feature subset. We employ this technique in combination with the k nearest neighbor classification rule, and compare the results with classical feature selection and extraction techniques, including sequential floating forward feature selection, and linear discriminant analysis. We also present results for the identification of favorable water-binding sites on protein surfaces     

Categorizing feature selection methods for multi-label classification

In many important application domains such as text categorization, biomolecular analysis, scene classification and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research on feature selection methods that allow the identification of relevant and informative features for multi-label classification. However, the methods proposed for this task are scattered in the literature, with no common framework to describe them and to allow an objective comparison. Here, we revisit a categorization of existing multi-label classification methods and, as our main contribution, we provide a comprehensive survey and novel categorization of the feature selection techniques that have been created for the multi-label classification setting. We conclude this work with concrete suggestions for future research in multi-label feature selection which have been derived from our categorization and analysis.     

Precise wavelet for current signature in 3 IM

Induction motors, which are used worldwide as the “workhorse” in industrial applications, are intermittently subjected to faults, mainly the stator faults. In this paper, fault diagnostics of induction motor using current signature analysis, with wavelet transform, is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, feature selection and classification. The feature extraction is done by wavelet transforms, using different wavelets which allow the use of long time intervals where there is precise low-frequency information, and shorter regions where there is precise high-frequency information. The extracted features are classified using the new generation pattern classification technique of Support Vector Machine (SVM) identification. Then the relative capability of the different wavelets, in performing the stator winding fault identification is analyzed and the best wavelet is selected.     

面向自动拆解的废旧电器整机智能识别方法

针对拆解废旧电器整机识别的传统方法效率低下的现象,提出一种自定义特征的废旧电器整机识别的方法;首先对废旧电器图像采用目标分割算法把废旧电器与背景进行分割,然后提取废旧电器整机的形状特征和卷积神经网络提取的深层特征,采用PCA算法对提取到的形状特征进行优化,将优化后的形状特征与深层特征进行特征拼接,最后将拼接后的特征向量对搭建好的3个SVM二分类器进行训练,得到废旧电器的分类模型;结果表明,拼接后的特征向量对废旧电器识别的准确率较高,高达91.21%,能够有效地实现废旧电器的智能识别。     

A discriminant analysis using composite features for classification problems

In this paper, we propose a new discriminant analysis using composite features for pattern classification. A composite feature consists of a number of primitive features, each of which corresponds to an input variable. The covariance of composite features is obtained from the inner product of composite features and can be considered as a generalized form of the covariance of primitive features. It contains information on statistical dependency among multiple primitive features. A discriminant analysis (C-LDA) using the covariance of composite features is a generalization of the linear discriminant analysis (LDA). Unlike LDA, the number of extracted features can be larger than the number of classes in C-LDA, which is a desirable property especially for binary classification problems. Experimental results on several data sets indicate that C-LDA provides better classification results than other methods based on primitive features.