基于一种多分类半监督学习算法的驾驶风格分类模型

基于驾驶模拟平台设计实验方案,同步采集驾驶员的驾驶操作信息和车辆状态信息,选取6个表征驾驶风格的特征参数,采用主成分分析(Principal Component Analysis,PCA)算法对多元特征参数进行特征提取,将前3个主成分作为驾驶风格识别模型的特征输入.利用K-means聚类完成样本标记工作.基于有监督支持向量机(Support Vector Machine,SVM)与多分类半监督学习算法(i MLCU)的原理,分别建立SVM与i MLCU驾驶风格识别模型,通过调节标记样本与未标记样本比例,对比使用不同样本比例训练的SVM和i MLCU模型的驾驶风格识别准确率.结果表明:相比于SVM,i MLCU表现出了更优异的驾驶风格识别能力,由此可知半监督i MLCU模型可以利用未标记样本提高模型对驾驶风格的识别能力.

无人机雷达航迹运动特征提取及组合分类方法

飞鸟和无人机目标的雷达回波存在高度相似性,区分难度较大。因此,对无人机、飞鸟以及动态降水杂波形成的目标航迹的时空间特征进行了研究,分析了无人机和飞鸟在运动机理以及行为模式上的差异,提出了一种基于目标航迹的运动特征提取方法,并构建了目标特征向量。基于探鸟雷达系统提供的目标实测航迹数据,建立了训练和测试样本集,采用监督类学习方法并结合随机森林模型实现了对无人机、飞鸟和降水杂波目标航迹的区分。实验结果表明,在广域范围内,无人机目标的正确识别率可达85%以上,分类器模型的运算效率高,样本适应性强,具备较好的普适性和实用价值。

一种有效的用于范例提取的改进聚类算法

针对传统范例提取算法随范例数增加而效率下降快的缺点 ,结合基于选择的 CL ARA聚类方法和 NCL聚类算法的优点 ,给出了一种有效的无监督聚类学习算法 .通过实验表明 ,该算法能在无监督下对范例进行准确归类 ,将它用于 CBR的范例提取中 。

一种新的自适应尺度近邻分类器

基于多类别监督学习,提出了一种局部自适应最近邻分类器。此方法使用椭球聚类学习方法估计有效尺度,用于拉长特征不明显的维,并限制特征重要的维。在修正的领域中,类条件概率按预期近似为常数,从而得到更好的分类性能。实验结果显示,对多类问题,这是一种有效且鲁棒的分类方法。

Heuristic feature selection method for clustering

In order to enable clustering to be done under a lower dimension, a new feature selection method for clustering is proposed. This method has three steps which are all carried out in a wrapper framework. First, all the original features are ranked according to their importance. An evaluation function E（f） used to evaluate the importance of a feature is introduced. Secondly, the set of important features is selected sequentially. Finally, the possible redundant features are removed from the important feature subset. Because the features are selected sequentially, it is not necessary to search through the large feature subset space, thus the efficiency can be improved. Experimental results show that the set of important features for clustering can be found and those unimportant features or features that may hinder the clustering task will be discarded by this method.

基于PVDF压电电缆的心冲击信号采集与自适应处理方法研究

为提高心率监测的实时性与准确性,研究并实现了一种基于PVDF压电电缆的非接触式心率检测系统。该系统针对心冲击信号(BCG)信号的特点设计了差分阈值寻峰算法进行J峰值的提取,在此基础上结合无监督学习中的聚类算法对BCG信号进行聚类分析,获取心跳模板u并与待测BCG信号进行匹配,进而实时给出心率值。与心电仪开展实验对比,心率测量误差约±2.65次/min,10 min内整体测量准确度约为96.64%,表明该监测系统在心率检测过程中具有较好的准确性和可靠性。

TWIN SUPPORT TENSOR MACHINES FOR MCS DETECTION

Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.

EEG classification based on probabilistic neural network with supervised learning in brain computer interface

Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.

Semi-supervised Long-tail Endoscopic Image Classification

Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited annotations.Method We explored semi-supervised long-tail endoscopic image classification in HyperKvasir,the largest gastrointestinal public dataset with 23 diverse classes.Semi-supervised learning algorithm FixMatch was applied based on consistency regularization and pseudo-labeling.After splitting the training dataset and the test dataset at a ratio of 4:1,we sampled 20%,50%,and 100% labeled training data to test the classification with limited annotations.Results The classification performance was evaluated by micro-average and macro-average evaluation metrics,with the Mathews correlation coefficient(MCC) as the overall evaluation.SSL algorithm improved the classification performance,with MCC increasing from 0.8761 to 0.8850,from 0.8983 to 0.8994,and from 0.9075 to 0.9095 with 20%,50%,and 100% ratio of labeled training data,respectively.With a 20% ratio of labeled training data,SSL improved both the micro-average and macro-average classification performance;while for the ratio of 50% and 100%,SSL improved the micro-average performance but hurt macro-average performance.Through analyzing the confusion matrix and labeling bias in each class,we found that the pseudo-based SSL algorithm exacerbated the classifier’ s preference for the head class,resulting in improved performance in the head class and degenerated performance in the tail class.Conclusion SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification,especially when the labeled data is extremely limited,which may benefit the building of assisted diagnosis systems for low-volume hospitals.However,the pseudo-labeling strategy may amplify the effect of class imbalance,which hurts the classification performance for the tail class.

Combining supervised classifiers with unlabeled data

Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabeled data are available.A label propagation based ensemble(LPBE) approach is proposed to further combine base classification results with unlabeled data.First,a graph is constructed by taking unlabeled data as vertexes,and the weights in the graph are calculated by correntropy function.Average prediction results are gained from base classifiers,and then propagated under a regularization framework and adaptively enhanced over the graph.The proposed approach is further enriched when small labeled data are available.The proposed algorithms are evaluated on several UCI benchmark data sets.Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.

Extending self-organizing maps for supervised classification of remotely sensed data

An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.

基于机器学习的图像分割研究

针对当前传统图像分割算法中对分割不清晰等问题,结合当前主流的及其学习算法,提出一种基于半监督均值聚类的图像分割算法。对此,文章首先对K-Means均值聚类的原理进行了深入分析,认为KMeans均值聚类属于一种无监督学习算法,具有快速聚类等特点,但是也存在簇类选择依靠经验,而没有科学的依据。其次针对传统图像分割算法中主要应用在低层图像处理中,而在高层图像处理应用较少的问题,结合半监督聚类算法的特点,通过对少数簇类利用标签进行信息标记,然后那个带有数据标签信息的Seeds集合进行中心分类,然后计算这些带有标签信息的集合与待标记类的距离,进而完成对不同特征的分类。最后,为验证上述方案的可行性,借助MATLAB编程软件对上述的算法进行编程,并以某视觉图像作为分割对象,和传统的聚类算法比较。结果表明本文设计算法可有效对图像进行分割,验证了其可行性和正确性,为图像分割理论的应用提供了实践参考与借鉴。

Performance analysis of new word weighting procedures for opinion mining

The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative in nature. These qualitative words need statistical computations to convert them into useful quantitative data. This data should be processed properly since it expresses opinions. Each of these opinion bearing words differs based on the significant meaning it conveys. To process the linguistic meaning of words into data and to enhance opinion mining analysis, we propose a novel weighting scheme, referred to as inferred word weighting(IWW). IWW is computed based on the significance of the word in the document(SWD) and the significance of the word in the expression(SWE) to enhance their performance. The proposed weighting methods give an analytic view and provide appropriate weights to the words compared to existing methods. In addition to the new weighting methods, another type of checking is done on the performance of text classification by including stop-words. Generally, stop-words are removed in text processing. When this new concept of including stop-words is applied to the proposed and existing weighting methods, two facts are observed:(1) Classification performance is enhanced;(2) The outcome difference between inclusion and exclusion of stop-words is smaller in the proposed methods, and larger in existing methods. The inferences provided by these observations are discussed. Experimental results of the benchmark data sets show the potential enhancement in terms of classification accuracy.