Benchmarking Analysis of the Accuracy of Classification Methods Related to Entropy

In the machine learning literature we can find numerous methods to solve classification problems. We propose two new performance measures to analyze such methods. These measures are defined by using the concept of proportional reduction of classification error with respect to three benchmark classifiers, the random and two intuitive classifiers which are based on how a non-expert person could realize classification simply by applying a frequentist approach. We show that these three simple methods are closely related to different aspects of the entropy of the dataset. Therefore, these measures account somewhat for entropy in the dataset when evaluating the performance of classifiers. This allows us to measure the improvement in the classification results compared to simple methods, and at the same time how entropy affects classification capacity. To illustrate how these new performance measures can be used to analyze classifiers taking into account the entropy of the dataset, we carry out an intensive experiment in which we use the well-known J48 algorithm, and a UCI repository dataset on which we have previously selected a subset of the most relevant attributes. Then we carry out an extensive experiment in which we consider four heuristic classifiers, and 11 datasets.     

Artificial neural network for suppression of banding artifacts in balanced steady-state free precession MRI

The balanced steady-state free precession (bSSFP) MR sequence is frequently used in clinics, but is sensitive to off-resonance effects, which can cause banding artifacts. Often multiple bSSFP datasets are acquired at different phase cycling (PC) angles and then combined in a special way for banding artifact suppression. Many strategies of combining the datasets have been suggested for banding artifact suppression, but there are still limitations in their performance, especially when the number of phase-cycled bSSFP datasets is small. The purpose of this study is to develop a learning-based model to combine the multiple phase-cycled bSSFP datasets for better banding artifact suppression. Multilayer perceptron (MLP) is a feedforward artificial neural network consisting of three layers of input, hidden, and output layers. MLP models were trained by input bSSFP datasets acquired from human brain and knee at 3T, which were separately performed for two and four PC angles. Banding-free bSSFP images were generated by maximum-intensity projection (MIP) of 8 or 12 phase-cycled datasets and were used as targets for training the output layer. The trained MLP models were applied to another brain and knee datasets acquired with different scan parameters and also to multiple phase-cycled bSSFP functional MRI datasets acquired on rat brain at 9.4T, in comparison with the conventional MIP method. Simulations were also performed to validate the MLP approach. Both the simulations and human experiments demonstrated that MLP suppressed banding artifacts significantly, superior to MIP in both banding artifact suppression and SNR efficiency. MLP demonstrated superior performance over MIP for the 9.4T fMRI data as well, which was not used for training the models, while visually preserving the fMRI maps very well. Artificial neural network is a promising technique for combining multiple phase-cycled bSSFP datasets for banding artifact suppression.     

Kernel representation-based nearest neighbor classifier

An improvement to the nearest neighbor classifier (INNC) has shown its excellent classification performance on some classification tasks. However, it is not very clearly known why INNC is able to obtain good performance and what the underlying classification mechanism is. Moreover, INNC cannot classify low-dimensional data well and some high-dimensional data in which sample vectors belonging to different class distribution but have the same vector direction. In order to solve these problems, this paper proposes a novel classification method, named kernel representation-based nearest neighbor classifier (KRNNC), which can not only remedy the drawback of INNC on low-dimensional data, but also obtain competitive classification results on high-dimensional data. We reveal the underlying classification mechanism of KRNNC in details, which can also be regarded as a theoretical supplement of INNC. We first implicitly map all samples into a kernel feature space by using a nonlinear mapping associated with a kernel function. Then, we represent a test sample as a linear combination of all training samples and use the representation ability to perform classification. From the way of classifying test samples, KRNNC can be regarded as the nonlinear extension of INNC. Extensive experimental studies on benchmark datasets and face image databases show the effectiveness of KRNNC.     

基于自适应无参经验小波变换和选择集成分类模型的运动想象

运动想象模式识别率的提高对脑机接口(BCI)技术的应用具有重要意义,本文采用自适应无参经验小波变换(APEWT)和选择集成分类模型相结合的方法提高脑电(EEG)信号的分类识别准确率.首先,通过APEWT将EEG信号分解成不同的模态;然后,使用最优模态重构后的信号计算其能量谱(ES)特征,使用最优模态分量计算其边际谱(MS)特征;最后,将不同时间段的ES特征和不同频段的MS特征输入到构建的选择集成分类模型中,从而得到其分类结果,并将该方法与其他4种组合方法进行比较.实验结果表明,本文方法具有较好分类准确率和实时性,其平均分类正确率高于其他4种方法,同时较近期使用相同数据的文献也有优势.本文为在线运动想象类BCI的应用提供了新的方法和思路.     

Multi-Classifier Fusion Based on MI–SFFS for Cross-Subject Emotion Recognition

With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing. Electroencephalography (EEG) can be used to detect the brain’s electrical activity associated with different emotions. The aim of this research is to improve the accuracy by enhancing the generalization of features. A Multi-Classifier Fusion method based on mutual information with sequential forward floating selection (MI_SFFS) is proposed. The dataset used in this paper is DEAP, which is a multi-modal open dataset containing 32 EEG channels and multiple other physiological signals. First, high-dimensional features are extracted from 15 EEG channels of DEAP after using a 10 s time window for data slicing. Second, MI and SFFS are integrated as a novel feature-selection method. Then, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) are employed to classify positive and negative emotions to obtain the output probabilities of classifiers as weighted features for further classification. To evaluate the model performance, leave-one-out cross-validation is adopted. Finally, cross-subject classification accuracies of 0.7089, 0.7106 and 0.7361 are achieved by the SVM, KNN and RF classifiers, respectively. The results demonstrate the feasibility of the model by splicing different classifiers’ output probabilities as a portion of the weighted features.     

Whether the Support Region of Three-Bit Uniform Quantizer Has a Strong Impact on Post-Training Quantization for MNIST Dataset?

Driven by the need for the compression of weights in neural networks (NNs), which is especially beneficial for edge devices with a constrained resource, and by the need to utilize the simplest possible quantization model, in this paper, we study the performance of three-bit post-training uniform quantization. The goal is to put various choices of the key parameter of the quantizer in question (support region threshold) in one place and provide a detailed overview of this choice’s impact on the performance of post-training quantization for the MNIST dataset. Specifically, we analyze whether it is possible to preserve the accuracy of the two NN models (MLP and CNN) to a great extent with the very simple three-bit uniform quantizer, regardless of the choice of the key parameter. Moreover, our goal is to answer the question of whether it is of the utmost importance in post-training three-bit uniform quantization, as it is in quantization, to determine the optimal support region threshold value of the quantizer to achieve some predefined accuracy of the quantized neural network (QNN). The results show that the choice of the support region threshold value of the three-bit uniform quantizer does not have such a strong impact on the accuracy of the QNNs, which is not the case with two-bit uniform post-training quantization, when applied in MLP for the same classification task. Accordingly, one can anticipate that due to this special property, the post-training quantization model in question can be greatly exploited.     

In vitro microemboli classification using neural network models and RF signals

Emboli classification is of high clinical importance for selecting appropriate treatment for patients. Several ultrasonic (US) methods using Doppler processing have been used for emboli detection and classification as solid or gaseous matter. We suggest in this experimental study exploiting the Radio-Frequency (RF) signal backscattered by the emboli since they contain additional information on the embolus than the Doppler signal. The aim of the study is the analysis of RF signals using Multilayer Perceptron (MLP) and Radial-Basis Function Network (RBFN) in order to classify emboli.Anthares scanner with RF access was used with a transmit frequency of 1.82 MHz at two mechanical indices (MI) 0.2 and 0.6. The mechanical index is given as the peak negative pressure (in MPa) divided by the square root of the frequency (in MHz). A Doppler flow phantom was used containing a 0.8 mm diameter vessel surrounded by a tissue mimicking material. To imitate gas emboli US behaviour, Sonovue microbubbles were injected at two different doses (10μl and 5μl) in a nonrecirculating at a constant flow. The surrounding tissue was assumed to behave as a solid emboli. In order to mimic real clinical pathological situations, Sonovue concentration was chosen such that the fundamental scattering from the tissue and from the contrast were identical. The amplitudes and bandwidths of the fundamental and the 2nd harmonic components were selected as input parameters to the MLP and RBFN models. Moreover the frequency bandwidths of the fundamental and the 2nd harmonic echoes were approximated by Gaussian functions and the coefficients were used as a third input parameter to the neural network models. The results show that the Gaussian coefficients provide the highest rate of classification in comparison to the amplitudes and the bandwidths of the fundamental and the 2nd harmonic components. The classification rates reached 89.28% and 92.85% with MLP and RBFN models respectively.This short communication demonstrates the opportunity to classify emboli based on a RF signals and neural network analysis.     

全谱段光谱分析的块状商品煤种类鉴别

常规的煤炭鉴别方法需进行繁琐的制样过程,且需结合多种化学参数指标进行综合判定,以得到较为准确的分析结果。提出一种基于500～2 350 nm的可见-近红外全谱段光谱分析技术与多层感知器(multilayer perceptron, MLP)分类方法相结合的块状商品煤鉴别方法。该方法具有非接触、无前期制样、无化学分析的优势,可快速高效的获取煤炭的分类信息。采用地物光谱仪采集煤炭原始光谱数据,对噪声过大、影响后续处理的谱段进行删除,剩余部分采用小波阈值去噪法进行噪声去除。将去噪后的数据分成三个数据集：可见-近红外光谱(500～900 nm)数据集、短波红外光谱(1 000～2 350 nm)数据集、全谱段光谱(500～2 350 nm)数据集。对以上三个数据集进行主成分分析,将提取出的25个主成分输入多层感知器分类模型。多层感知器模型由输入层、隐藏层(两层)、softmax分类器构成。对三个数据集进行分类精度的对比,并采用随机森林(random forest, RF)与支持向量机(support vector machine, SVM)两种分类算法进行进一步的验证分析。结果表明：对块状商品煤分类,全谱段光谱分析技术由于数据信息量丰富,能够得到更优的分类效果,在训练样本数为132时,采用MLP分类器的分类精度最高,为98.03%;随机森林与SVM的分类结果验证了全谱段数据集的优越性与普适性。该研究为煤炭的在线分析、便携式煤炭检测仪器的研发提供了可靠的技术支持。     

点云投影结合轻量化卷积神经网络实现三维成像声呐快速目标分类*

三维成像声呐的成像结果是三维点云,基于点云的三维成像声呐目标分类方法具有网络结构复杂,计算量大的特点,针对这一问题本文提出了一种将三维成像声呐成像结果从三维点云投影至二维图像的方法,并且使用轻量化卷积神经网络实现了三维成像声呐快速目标分类。该方法首先对三维成像声呐波束形成后的波束域数据进行最大值滤波和阈值滤波,降低点云数据维度;接着,依据三维成像声呐的波束方向,将点云投影为深度图和强度图,分别保存点云的位置信息和强度信息;然后,利用深度图和强度图分别作为第一个通道和第二个通道构建混合通道图,将混合通道图作为目标分类网络的输入,从而将三维点云的目标分类问题转换为二维图像的目标分类问题;最后使用MobileNetV2网络实现了三维成像声呐快速目标分类。实验结果表明,通过本文提出的投影方法可以用二维图像分类网络完成三维成像声呐点云的目标分类任务;而且混合通道图比单独的强度图和深度图收敛速度更快,结合目标识别网络可以25fps实时的进行目标分类,在真实数据集上分类精度达到了91.13%。     

基于核的k-最近邻在水下目标识别中的应用*

针对水中目标特征类型多、非线性强的特点,本文将K-KNN应用于水中目标识别。该方法采用PCA对特征矩阵进行降维,利用Kernel技巧将降维后的特征映射到高维空间进行KNN分类识别,并讨论了邻近点个数K对试验结果的影响。实际试验数据验证结果表明：与传统的KNN和BP神经网络分类器相比,K-KNN分类器的综合性能更优。     

水下高分辨率声图中小目标的深度网络分类方法

针对声成像数据缺少条件下的水下沉底小目标分类问题,提出一种深度网络分类算法。首先,采用高斯混合模型对声影区统计特性进行建模并提取声图阴影,在此基础上构建仿真数据集和真实数据集。将仿真数据集输入卷积神经网络进行训练,保留其特征提取部分,用于对真实数据集进行特征提取.重建网络分类部分并采用真实数据集的特征向量进行训练。结果表明,所提出的方法分类正确率可达88.24%,与6种对照方法相比平均分类正确率分别提升8.67%,20.47%,19.78%,11.59%,9.01%,11.58%。验证了所提出方法在小样本条件下具有较好对水下沉底小目标的分类能力。其学习曲线收敛到96.25%,仅比验证曲线高5.14%,说明在一定程度上缓解了过拟合问题。将改进的卷积神经网络应用于融合分类器,通过与逻辑回归分类器、支持向量机对目标进行分类并融合决策,正确率为93.33%,可进一步提高算法的正确率和稳定性.     

Chaotic biogeography-based optimization approach to target detection in UAV surveillance

This paper describes a novel chaotic biogeography-based optimization (CBBO) algorithm for target detection by means of template matching to meet the request of unmanned aerial vehicle (UAV) surveillance. Template matching has been widely applied in movement tracking and other fields and makes excellent performances in visual navigation. Biogeography-based optimization (BBO) algorithm emerges as a new kind of optimization method on the basis of biogeography concept. The idea of migration and mutation strategy of species in BBO contributes to solving optimization problems. Our work adds chaotic searching strategy into BBO and applies CBBO in template matching. By utilizing chaotic strategy, the population ergodicity and global searching ability are improved, thus avoiding local optimal solutions during evolution. Applying the algorithm to resolving template matching problem overcomes the defects of common image matching. Series of experimental results demonstrate the feasibility and effectiveness of our modified approach over other algorithms in solving template matching problems. Our modified BBO algorithm performs better in terms of convergence property and robustness when compared with basic BBO.     

Blast noise classification with common sound level meter metrics

A common set of signal features measurable by a basic sound level meter are analyzed, and the quality of information carried in subsets of these features are examined for their ability to discriminate military blast and non-blast sounds. The analysis is based on over 120?000 human classified signals compiled from seven different datasets. The study implements linear and Gaussian radial basis function (RBF) support vector machines (SVM) to classify blast sounds. Using the orthogonal centroid dimension reduction technique, intuition is developed about the distribution of blast and non-blast feature vectors in high dimensional space. Recursive feature elimination (SVM-RFE) is then used to eliminate features containing redundant information and rank features according to their ability to separate blasts from non-blasts. Finally, the accuracy of the linear and RBF SVM classifiers is listed for each of the experiments in the dataset, and the weights are given for the linear SVM classifier.     

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.     

利用带无标签数据的双支持向量机对恒星光谱分类

恒星光谱分类是天文技术与方法领域一直关注的热点问题之一。随着观测设备持续运行和不断改进,人类获得的光谱数量与日俱增。这些海量光谱为人工处理带来了极大挑战。鉴于此,研究人员开始关注数据挖掘算法,并尝试对这些光谱进行数据挖掘。近年来,神经网络、自组织映射、关联规则等数据挖掘方法广泛应用于恒星光谱分类。在这些方法中,支持向量机(SVM)以其强大的学习能力和高效的分类性能而备受推崇。SVM的基本思想是试图在两类样本之间找到一个最优分类面将两类分开。SVM在求解时,通过将其最优化问题转化为具有(QP)形式的凸问题,进而得到全局最优解。尽管该方法在实际应用中表现优良,但为了进一步提高其分类能力,有的学者提出双支持向量机(TSVM)。该方法通过构造两个非平行的分类面将两类分开,每一类靠近某个分类面,而远离另一个分类面。TSVM的计算效率较之传统SVM提高近4倍,因此,自TSVM提出后便受到研究人员的持续关注,并出现若干改进算法。在恒星光谱分类中,一般分类算法都是根据历史观测光谱来建立分类模型,其中最关键的是对光谱进行人工标注,这项工作极为繁琐,且容易犯错。如何利用已标记的光谱以及部分无标签的光谱来建立分类模型显得尤为重要。因此,提出带无标签数据的双支持向量机(TSVMUD)用以实现对恒星光谱智能分类的目的。该方法首先将光谱分为训练数据集和测试数据集两部分;然后,在训练集上进行学习,得到分类依据;最后利用分类依据对测试集上的光谱进行验证。继承了双支持向量机的优势,更重要的是,在训练集上学习分类模型过程中,不仅考虑有标记的训练样本,也考虑部分未标记的样本。一方面提高了学习效率,另一方面得到更优的分类模型。在SDSS DR8恒星光谱数据集上的比较实验表明,与支持向量机SVM、双支持向量机TSVM以及K近邻(KNN)等传统分类方法相比,带无标签数据的双支持向量机TSVMUD具有更优的分类能力。然而,该方法亦存在一定的局限性,其中一大难题是其无法处理海量光谱数据。该工作将借鉴海量数据随机采样思想,利用大数据处理技术,来对所提方法在大数据环境下的适应性展开进一步研究。     

基于蝙蝠回波定位模型的材质识别方法研究

在蝙蝠的回波定位和目标识别模型的基础上完成了目标材质识别的工作。利用超声换能器发射超声脉冲，接收回波信号并对其进行处理；分别提取了时域包络、功率谱和短时傅里叶（STFT）系数作为BP（Back—Propagation）网络分类器的输入特征向量；完成了对纸板、泡沫板和木板的识别工作。实验结果表明识别正确率最高达到近90％。     

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were established from different points of view. The present study is initiated in an attempt to review the state-of-the-art ensemble classification algorithms for dealing with imbalanced datasets, offering a comprehensive analysis for incorporating the dynamic selection of base classifiers in classification. By conducting 14 existing ensemble algorithms incorporating a dynamic selection on 56 datasets, the experimental results reveal that the classical algorithm with a dynamic selection strategy deliver a practical way to improve the classification performance for both a binary class and multi-class imbalanced datasets. In addition, by combining patch learning with a dynamic selection ensemble classification, a patch-ensemble classification method is designed, which utilizes the misclassified samples to train patch classifiers for increasing the diversity of base classifiers. The experiments’ results indicate that the designed method has a certain potential for the performance of multi-class imbalanced classification.     

利用高性能混合深度学习网络提升光谱分类性能研究

随着观测设备的不断完善,人们获得的光谱数量持续上升,如何进一步提高光谱自动分类的性能引起广泛关注.为此,以恒星光谱为研究对象,在近年来新出现的BERT和CNN等深度学习模型的基础上,试图融合了BERT模型和CNN模型在特征提取和智能分类方面的优势,提出高性能混合深度学习网络BERT-CNN,用以探讨该模型在提升光谱分类...     

基于Gammatone滤波器组时频谱和卷积神经网络的海底底质分类

为了有效利用海底底质信号完成海底底质的分类识别,该文提出一种将深度学习方法和底质信号相结合实现底质分类识别的方法.首先利用Gammatone滤波器组计算底质侧扫图像信号的时频谱,然后通过卷积神经网络对得到的时频谱进行分类识别完成底质分类.利用加利福尼亚州Scott Creek近海采集的侧扫声呐图像数据进行数据分析,结果...