Study of the feasibility of distinguishing cigarettes of different brands using an Adaboost algorithm and near-infrared spectroscopy

The feasibility of utilizing an Adaboost algorithm in conjuction with near-infrared (NIR) spectroscopy to automatically distinguish cigarettes of different brands was explored. Simple linear discriminant analysis (LDA) was used as the base algorithm to train all weak classifiers in Adaboost. Both principal component analysis (PCA) and its kernel version (kernel principal component analysis, KPCA) were used for feature extraction and were also compared to each other. The influence of the training set size on the final classification model was also investigated. Using a case study, it was demonstrated that Adaboost coupled with PCA or KPCA can obviously improve the ability to discriminate between samples that cannot be separated by a single linear classifier. However, in term of the overall performance, KPCA appears preferable to PCA for feature extraction, especially when the samples used for training are relatively small. The results also indicate that more training samples should be applied, if possible, in order to fully demonstrate the superiority of Adaboost. It seems that the use of an Adaboost algorithm in conjunction with NIR spectroscopy in combination with KPCA for feature extraction comprises a promising tool for distinguishing cigarettes of different brands, especially in situations where there is an obvious overlap between the NIR spectra afforded by cigarettes of different brands.     

Robust infrared target tracking using discriminative and generative approaches

The process of designing an efficient tracker for thermal infrared imagery is one of the most challenging tasks in computer vision. Although a lot of advancement has been achieved in RGB videos over the decades, textureless and colorless properties of objects in thermal imagery pose hard constraints in the design of an efficient tracker. Tracking of an object using a single feature or a technique often fails to achieve greater accuracy. Here, we propose an effective method to track an object in infrared imagery based on a combination of discriminative and generative approaches. The discriminative technique makes use of two complementary methods such as kernelized correlation filter with spatial feature and AdaBoost classifier with pixel intesity features to operate in parallel. After obtaining optimized locations through discriminative approaches, the generative technique is applied to determine the best target location using a linear search method. Unlike the baseline algorithms, the proposed method estimates the scale of the target by Lucas-Kanade homography estimation. To evaluate the proposed method, extensive experiments are conducted on 17 challenging infrared image sequences obtained from LTIR dataset and a significant improvement of mean distance precision and mean overlap precision is accomplished as compared with the existing trackers. Further, a quantitative and qualitative assessment of the proposed approach with the state-of-the-art trackers is illustrated to clearly demonstrate an overall increase in performance.     

基于Adaboost+OLDA和近红外光谱的猪肉贮藏时间辨别

猪肉的贮藏时间和猪肉的新鲜度紧密相关。通过近红外漫反射光谱技术获取猪肉样本数据,利用正交线性判别分析(OLDA)算法进行特征提取,同时将自适应提升法(Adaboost)引入OLDA,提出了一种基于Adaboost和OLDA的集成学习算法——Adaboost+OLDA。实验针对分类正确率和运算时间将传统特征提取算法(PCA+LDA和OLDA)和Adaboost+OLDA算法进行了对比研究,结果表明Adaboost+OLDA算法不仅具有很好的运算效率,而且提高了OLDA算法的泛化能力,在猪肉样本测试中达到了95%以上的分类正确率。     

自适应增强方法在光谱自动分类中的应用

针对活动星系核(AGN)光谱中发射线的不同特征,在恢复到静止系状态后的光谱上截取具有有效特征的波段范围,采用自适应增强(Adaboost)的方法,对宽线和窄线AGNs进行特征融合的分类实验,经分析,确定了以Hα和[NⅡ]发射线为主的波段为宽线和窄线AGNs光谱的主要区别特征。再单独对Hα和[NⅡ]发射线为主的波段,用自适应增强的方法对其进行光谱分类。自适应增强方法在训练过程中不断地加入“弱分类器”,直到达到某个预定的足够小的误差率或一定的循环次数,最后构成的总体分类器的分类判决由这些“弱分类器”各自的判决结果的投票来决定。此方法不需要事先调节参数,且“弱分类器”的分类结果只需好于随机猜测,算法简单。实验证明,对于单独采用以Hα和[NⅡ]发射线为主的波段,自适应增强方法能达到较好的分类效果,从而可有效地应用于大型光谱巡天所产生的活动星系核光谱的自动分类中。     

A Novel Method to Determine Basic Probability Assignment Based on Adaboost and Its Application in Classification

In the framework of evidence theory, one of the open and crucial issues is how to determine the basic probability assignment (BPA), which is directly related to whether the decision result is correct. This paper proposes a novel method for obtaining BPA based on Adaboost. The method uses training data to generate multiple strong classifiers for each attribute model, which is used to determine the BPA of the singleton proposition since the weights of classification provide necessary information for fundamental hypotheses. The BPA of the composite proposition is quantified by calculating the area ratio of the singleton proposition’s intersection region. The recursive formula of the area ratio of the intersection region is proposed, which is very useful for computer calculation. Finally, BPAs are combined by Dempster’s rule of combination. Using the proposed method to classify the Iris dataset, the experiment concludes that the total recognition rate is 96.53% and the classification accuracy is 90% when the training percentage is 10%. For the other datasets, the experiment results also show that the proposed method is reasonable and effective, and the proposed method performs well in the case of insufficient samples.     

基于Adaboost及谱回归判别分析的近红外光谱固态发酵过程状态识别

为了实现固态发酵过程状态的快速监测,以饲料蛋白固态发酵为实验对象,开展了基于近红外光谱分析技术的饲料蛋白固态发酵过程状态定性识别研究。首先利用Antaris Ⅱ型傅里叶变换近红外光谱仪采集140个固态发酵物样本的近红外光谱,并采用标准正态变换(SNV)光谱预处理方法对获得的原始光谱进行预处理;其次,采用谱回归判别分析(SRDA)法对预处理后的近红外光谱进行特征提取;最后,采用最近邻(NN)分类算法作为弱分类器建立固态发酵过程状态识别模型,并对测试集样本进行识别。结果显示,与利用主成分分析(PCA)法和线性判别分析(LDA)法提取的光谱特征建立的识别模型结果相比较,SRDA-NN识别模型获得的结果最佳,在测试集中的正确识别率达到94.28%;为了进一步提高识别模型的准确率,将自适应提升法(Adaboost)与SRDA-NN方法结合,提出了Adaboost-SRDA-NN集成学习算法来建立饲料蛋白固态发酵过程状态的在线监测模型。通过Adaboost算法提升后的SRDA-NN模型预测性能得到了进一步增强,Adaboost-SRDA-NN模型在测试集中的正确识别率达到100%。试验结果表明：在近红外光谱定性分析模型校正过程中,SRDA方法能有效地对近红外光谱数据进行特征提取,以实现维数约简;另外,Adaboost算法能很好地提升最终分类模型的预测精度。     

基于EEMD-Elman-Adaboost的中美股票价格预测研究

针对股票价格序列高度非正态、非线性、非平稳等复杂特征,文章以Elman神经网络为基础,引入集合经验模态分解(EEMD)与Adaboost算法,对中美股票的日收盘价进行预测。首先,利用EEMD算法将样本分解为多个本征模函数分量和1个残差分量。其次,用Adaboost算法优化Elman神经网络,对各个分量进行预测。最后,将各分量预测结果进行求和,作为最终预测结果。研究结果表明：EEMD-Elman-Adaboost模型对中美股票价格预测的均方根误差、平均相对误差、平均绝对误差均比现有的BP、Elman、EMD-Elman、EEMD-Elman模型小,新组合模型融合了EEMD、Elman神经网络、Adaboost算法的优点,具有更强的泛化能力和跟随能力。     

基于模糊Adaboost算法的支持向量回归机

针对单一支持向量回归机预测精度不十分良好的问题,结合Adaboost算法以及引入隶属函数,提出了一个基于模糊Aaboost算法的支持向量回归机模型。将该模型应用于金融时间序列预测问题的实验表明,预测精度有一定的提高,从而说明了该模型的有效性和可行性。     

车牌识别机器学习方法的数学建模与实现

首先,针对不同光照、复杂背景和投影失真的车牌图像建立基于Adaboost算法和改进Haar特征的车牌检测模型;然后,运用Radon变换进行车牌校正,并结合3次B样条小波变换和识别反馈模型对字符进行粗和精分割;最后,根据汉字和数字字母的不同结构特征,采用不同的算法提取特征,特别是针对车牌字符特点,训练汉字、字母和数字字母3种神经网络模型用于建立字符识别模型.实验结果表明该模型是实用的.     

C-iSUMO: A sumoylation site predictor that incorporates intrinsic characteristics of amino acid sequences

Post-translational modifications are considered important molecular interactions in protein science. One of these modifications is “sumoylation” whose computational detection has recently become a challenge. In this paper, we propose a new computational predictor which makes use of the sine and cosine of backbone torsion angles and the accessible surface area for predicting sumoylation sites. The aforementioned features were computed for all the proteins in our benchmark dataset, and a training matrix consisting of sumoylation and non-sumoylation sites was ultimately created. This training matrix was balanced by undersampling the majority class (non-sumoylation sites) using the NearMiss method. Finally, an AdaBoost classifier was used for discriminating between sumoylation and non-sumoylation sites. Our predictor was called “C-iSumo” because of its effective use of circular functions. C-iSumo was compared with another predictor which was outperformed in statistical metrics such as sensitivity (0.734), accuracy (0.746) and Matthews correlation coefficient (0.494).