A hybrid classifier based on support vector machine and Jaya algorithm for breast cancer classification

Neural Computing and Applications - The experts’ decisions and evaluating the patients’ data are the most significant parts affecting the breast cancer analysis. For early breast cancer...     

基于支持向量机的粗糙分类器

支持向量机和粗糙集理论是两种分类技术.前者寻求最大化两类间隔的最优分类超平面,后者用逻辑规则解释分类.基于两者的关系,提出了一种复合算法,且将其推广到回归.新算法在一定程度降低了计算复杂度,且适用于软间隔分类.数值实验表明新算法是有效可行的.     

优化的支持向量机集成分类器在非平衡数据集分类中的应用

传统的分类算法大都建立在平衡数据集的基础上,当样本数据不平衡时,这些学习算法的性能往往会明显下降.对于非平衡数据分类问题,提出了一种优化的支持向量机(SVM)集成分类器模型,采用KSMOTE和Bootstrap对非平衡数据进行预处理,生成相应的SVM模型并用复合形算法优化模型参数,最后利用优化的参数并行生成SVM集成分类器模型,采用投票机制得到分类结果.对5组UCI标准数据集进行实验,结果表明采用优化的SVM集成分类器模型较SVM模型、优化的SVM模型等分类精度有了明显的提升,同时验证了不同的bootNum取值对分类器性能效果的影响.     

A novel multi-parameter support vector machine for image classification

The support vector machine (SVM) classification algorithm has received increasing attention in recent years in remote sensing for land-cover classification. However, it is well known that the performance of the SVM is sensitive to the choice of parameter settings. The traditional single optimized parameter SVM (SOP-SVM) attempts to identify globally optimized parameters for multi-class land-cover classification. In this article, a novel multi-parameter SVM (MP-SVM) algorithm is proposed for image classification. It divides the training set into several subsets, which are subsequently combined. Based on these combinations, sub-classifiers are constructed using their own optimum parameters, providing votes for each pixel with which to construct the final output. The SOP-SVM and MP-SVM were tested on three pilot study sites with very high, high, and low levels of landscape complexity within the Sanjiang Plain – a typical inland wetland and freshwater ecosystem in northeast China. A high overall accuracy of 82.19% with kappa coefficient (κ) of 0.80 was achieved by the MP-SVM in the very high-complexity landscape, statistically significantly different (z-value = 3.77) from the overall accuracy of 72.50% and κ of 0.69 produced by the traditional SOP-SVM. Besides, for the moderate-complexity landscape a significant increase in accuracy was achieved (z-value = 2.44), with overall accuracy of 84.03% and κ of 0.80 compared with an overall accuracy 76.05% and κ of 0.71 for the SOP-SVM. However, for the low-complexity landscape the MP-SVM was not significantly different from the SOP-SVM (z-value = 0.80). Thus, the results suggest that the MP-SVM method is promising for application to very high and high levels of landscape complexity, differentiating complex land-cover classes that are spectrally mixed, such as marsh, bare land, and meadow.     

基于核的大间距分类器

提出了一种新的非线性鉴别分析算法-基于核的大间距分类器,该算法的主要思想是将原始样本映射到更高维的空间中,利用核技术对传统的大间距分类算法进行改进,在新的高维空间中利用再生核技术寻找核鉴别矢量,使得在这个新的空间中核类内散度尽可能的小.在ORL人脸数据库上做实验,分别对识别率及识别时间做分析,可以看出本方法的优势所在.     

Constrained classifier: a novel approach to nonlinear classification

Simple classifiers have the advantage of more generalization capability with the side effect of less power. It would be a good idea if we could build a classifier which is as simple as possible while giving it the ability of classifying complex patterns. In this paper, a hybrid classifier called “constrained classifier” is presented that classifies most of the input patterns using a simple, for example, a linear classifier. It performs the classification in four steps. In the “Dividing” step, the input patterns are divided into linearly separable and nonlinearly separable groups. The patterns belonging to the first group are classified using a simple classifier while the second group patterns (named “constraints”) are modeled in the “Modeling” step. The results of previous steps are merged together in the “Combining” step. The “Evaluation” step tests and fine tunes the membership of patterns into two groups. The experimental results of comparison of the new classifier with famous classifiers such as “support vector machine”, k-NN, and “Classification and Regression Trees” are very encouraging.     

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。     

Text classification: A least square support vector machine approach

This paper presents a least square support vector machine (LS-SVM) that performs text classification of noisy document titles according to different predetermined categories. The system's potential is demonstrated with a corpus of 91,229 words from University of Denver's Penrose Library catalogue. The classification accuracy of the proposed LS-SVM based system is found to be over 99.9%. The final classifier is an LS-SVM array with Gaussian radial basis function (GRBF) kernel, which uses the coefficients generated by the latent semantic indexing algorithm for classification of the text titles. These coefficients are also used to generate the confidence factors for the inference engine that present the final decision of the entire classifier. The system is also compared with a K-nearest neighbor (KNN) and Naïve Bayes (NB) classifier and the comparison clearly claims that the proposed LS-SVM based architecture outperforms the KNN and NB based system. The comparison between the conventional linear SVM based classifiers and neural network based classifying agents shows that the LS-SVM with LSI based classifying agents improves text categorization performance significantly and holds a lot of potential for developing robust learning based agents for text classification.     

基于空间支持向量域分类器的人脸识别

提出了一种基于小波变换、奇异值分解与空间支持向量域分类器相结合的人脸识别方法。在使用空间支持向量分类器对不同人脸图像的奇异特征向量进行分类时,计算所测样本到各个超球球心的距离,并根据其与超球半径的关系来判断其所归属。并在ORL人脸数据库中进行实验。实验表明提出的人脸识别方法识别精度可达97.5%。     

决策树支持向量机多分类器设计的向量投影法

针对如何有效地设计决策树支持向量机(SVM)多类分类器的层次结构这个关键问题,提出一种基于向量投影的类间可分性测度的设计方法,并给出一种基于该类间可分性测度设计决策树SVM多分类器层次结构的方法.为加快每个SVM子分类器的训练速度且保持其高推广性,将基于向量投影的支持向量预选取方法用于每个子分类器的训练中.通过对3个大规模数据集和手写体数字识别的仿真实验表明,新方法能有效地提高决策树SVM多类分类器的分类精度和速度.     

Handedness tests for preschool children: A novel approach based on graphics tablets and support vector machines

Handedness is a certain kind of laterality, namely the preference of humans for a certain hand (the dominant hand). Reliable handedness tests are needed in various contexts, for example, to avoid a wrong writing education of children. In this article, we propose a new approach for a gradual rating of hand proficiency which consists of three subtests and is suited for preschool children. We demonstrate the benefits of using a graphics tablet for handedness tests, investigate the advantages of the three subtests and their combination, and outline the possibility of interpreting the gradual output of a classifier. The classification is based on ensembles of support vector machines that are trained with sample data. Input of these classifiers are attributes that reflect various aspects of hand motor skills. The most relevant input attributes of the classifiers are selected from a large set of possible attributes with a ranking technique based on the Gini index. We evaluate this approach using a data set with data gathered from 53 preschool children aged between five and six and a half (45 with certain and known handedness, 8 with uncertain or ambiguous handedness).     

Fuzzy c-means based support vector machines classifier for perfume recognition

Identification of more than three perfumes is very difficult for the human nose. It is also a problem to recognize patterns of perfume odor with an electronic nose that has multiple sensors. For this reason, a new hybrid classifier has been presented to identify type of perfume from a closely similar data set of 20 different odors of perfumes. The structure of this hybrid technique is the combination of unsupervised fuzzy clustering c-mean (FCM) and supervised support vector machine (SVM). On the other hand this proposed soft computing technique was compared with the other well-known learning algorithms. The results show that the proposed hybrid algorithm’s accuracy is 97.5% better than the others.     

Investigating fuzzy-input fuzzy-output support vector machines for robust voice quality classification

The dynamic use of voice qualities in spoken language can reveal useful information on a speakers attitude, mood and affective states. This information may be very desirable for a range of, both input and output, speech technology applications. However, voice quality annotation of speech signals may frequently produce far from consistent labeling. Groups of annotators may disagree on the perceived voice quality, but whom should one trust or is the truth somewhere in between? The current study looks first to describe a voice quality feature set that is suitable for differentiating voice qualities on a tense to breathy dimension. Further, the study looks to include these features as inputs to a fuzzy-input fuzzy-output support vector machine (F²SVM) algorithm, which is in turn capable of softly categorizing voice quality recordings. The F²SVM is compared in a thorough analysis to standard crisp approaches and shows promising results, while outperforming for example standard support vector machines with the sole difference being that the F²SVM approach receives fuzzy label information during training. Overall, it is possible to achieve accuracies of around 90% for both speaker dependent (cross validation) and speaker independent (leave one speaker out validation) experiments. Additionally, the approach using F²SVM performs at an accuracy of 82% for a cross corpus experiment (i.e. training and testing on entirely different recording conditions) in a frame-wise analysis and of around 97% after temporally integrating over full sentences. Furthermore, the output of fuzzy measures gave performances close to that of human annotators.     

基于支持向量机的流量分类方法*

针对现有流量分类方法存在的准确率低、应用范围受限、计算复杂度高等问题,提出使用支持向量机方法来解决流量分类问题。使用公开的人工标注数据集作为训练集和测试集,通过有监督学习构建支持向量机流量分类器。此外,通过实验进一步分析了训练集大小、核函数、惩罚因子等因素对支持向量机分类性能的影响。实验结果表明支持向量机分类器可以达到98%以上的流分类准确率。     

基于支持向量机和输出编码的文本分类器研究

介绍了一种支持向量机与输出编码相结合的文本分类器算法 ,采用一对多、一对一和纠错编码三种编码方式以及相似度计算的海明码距、边界损失方法进行文本分类和测试 ,表明一对多编码与边界损失相似度计算相结合的分类器系统具有最高的查全率和查准率。     

One-against-one fuzzy support vector machine classifier: An approach to text categorization

The growth of the internet information delivery has made automatic text categorization essential. This investigation explores the challenges of multi-class text categorization using one-against-one fuzzy support vector machine with Reuter’s news as the example data. The performances of four different membership functions on one-against-one fuzzy support vector machine are measured using the macro-average performance indices. Analytical results indicate that the proposed method achieves a comparable or better performance than the one-against-one support vector machine.     

A support vector machine classifier with automatic confidence and its application to gender classification

In this paper, we propose a support vector machine with automatic confidence (SVMAC) for pattern classification. The main contributions of this work to learning machines are twofold. One is that we develop an algorithm for calculating the label confidence value of each training sample. Thus, the label confidence values of all of the training samples can be considered in training support vector machines. The other one is that we propose a method for incorporating the label confidence value of each training sample into learning and derive the corresponding quadratic programming problems. To demonstrate the effectiveness of the proposed SVMACs, a series of experiments are performed on three benchmarking pattern classification problems and a challenging gender classification problem. Experimental results show that the generalization performance of our SVMACs is superior to that of traditional SVMs.     

基于超球体多类支持向量数据描述的医学图像分类新方法

针对乳腺X光医学图像多分类问题中训练速度比较慢的问题,提出超球体多分类支持向量数据描述(HSMC-SVDD)分类算法,即把超球体单分类支持向量数据描述直接扩展到超球体多分类支持向量数据描述。通过对乳腺X光图像提取灰度共生矩阵特征;然后用核主成分分析(KPCA)对数据进行降维;最后用超球体多分类支持向量数据描述分类器进行分类。由于每一类样本只参与构造一个超球体的训练,因此训练速度明显提高。实验结果表明,这种超球体多分类支持向量数据描述分类器的平均训练时间为21.369s,训练时间比Wei等(WEI L Y, YANG Y Y, NISHIKAWA R M,et al.A study on several machine-learning methods for classification of malignant and benign clustered micro-calcifications. IEEE Transactions on Medical Imaging, 2005, 24(3): 371-380)提出的组合分类器（平均训练时间40.2s）减少了10~20s,分类精度最高达76.6929%,适合解决类别数较多的分类问题。     

A novel framework for multi-class classification via ternary smooth support vector machine

This paper extends the previous work in smooth support vector machine (SSVM) from binary to k-class classification based on a single-machine approach and call it multi-class smooth SVM (MSSVM). This study implements MSSVM for a ternary classification problem and labels it as TSSVM. For the case k>3, this study proposes a one-vs.-one-vs.-rest (OOR) scheme that decomposes the problem into k(k−1)/2 ternary classification subproblems based on the assumption of ternary voting games. Thus, the k-class classification problem can be solved via a series of TSSVMs. The numerical experiments in this study compare the classification accuracy for TSSVM/OOR, one-vs.-one, one-vs.-rest schemes on nine UCI datasets. Results show that TSSVM/OOR outperforms the one-vs.-one and one-vs.-rest for all datasets. This study includes further error analyses to emphasize that the prediction confidence of OOR is significantly higher than the one-vs.-one scheme. Due to the nature of OOR design, it can detect the hidden (unknown) class directly. This study includes a “leave-one-class-out” experiment on the pendigits dataset to demonstrate the detection ability of the proposed OOR method for hidden classes. Results show that OOR performs significantly better than one-vs.-one and one-vs.-rest in the hidden-class detection rate.