Prototype reduction schemes applicable for non-stationary data sets

All of the prototype reduction schemes (PRS) which have been reported in the literature, process time-invariant data to yield a subset of prototypes that are useful in nearest-neighbor-like classification. Although these methods have been proven to be powerful, they suffer from a major disadvantage when they are utilized for applications involving non-stationary data, namely, time varying samples, typical of video and multimedia applications. In this paper, we suggest two PRS mechanisms which, in turn, are suitable for two distinct models of non-stationarity. In the first model, the data points obtained at discrete time steps, are individually assumed to be perturbed in the feature space, because of noise in the measurements or features. As opposed to this, in the second model, we assume that, at discrete time steps, new data points are available, and that these themselves are generated due to a non-stationarity in the parameters of the feature space. In both of these cases, rather than process all the data as a whole set using a PRS, we propose that the information gleaned from a previous PRS computation be enhanced to yield the prototypes for the current data set using an LVQ-3 type “fine tuning”. The results are, to our knowledge, the first reported PRS results for non-stationary data, and can be summarized as follows: if the system obeys the first model of non-stationarity, the improved accuracy is as high as 90.98% for artificial data “Non_normal 2”, and as high as 97.62% for the real-life data set, “Arrhythmia”. As opposed to this, if the system obeys the second model of non-stationarity, the improved accuracy is as high as 76.30% for the artificial data, and as high as 97.40% for this real-life data set. These are, in our opinion, very impressive, considering that the data sets are truly time-varying.     

On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods

The subspace method of pattern recognition is a classification technique in which pattern classes are specified in terms of linear subspaces spanned by their respective class-based basis vectors. To overcome the limitations of the linear methods, kernel-based nonlinear subspace (KNS) methods have been recently proposed in the literature. In KNS, the kernel principal component analysis (kPCA) has been employed to get principal components, not in an input space, but in a high-dimensional space, where the components of the space are nonlinearly related to the input variables. The length of projections onto the basis vectors in the kPCA are computed using a kernel matrix K, whose dimension is equivalent to the number of sample data points. Clearly this is problematic, especially, for large data sets.In this paper, we suggest a computationally superior mechanism to solve the problem. Rather than define the matrix K with the whole data set and compute the principal components, we propose that the data be reduced into a smaller representative subset using a prototype reduction scheme (PRS). Since a PRS has the capability of extracting vectors that satisfactorily represent the global distribution structure, we demonstrate that data points which are ineffective in the classification can be eliminated to obtain a reduced kernel matrix, K, without degrading the performance. Our experimental results demonstrate that the proposed mechanism dramatically reduces the computation time without sacrificing the classification accuracy for samples involving real-life data sets as well as artificial data sets. The results especially demonstrate the computational advantage for large data sets, such as those involved in data mining and text categorization applications.     

Enhancing prototype reduction schemes with recursion: a method applicable for "large" data sets

Most of the prototype reduction schemes (PRS), which have been reported in the literature, process the data in its entirety to yield a subset of prototypes that are useful in nearest-neighbor-like classification. Foremost among these are the prototypes for nearest neighbor classifiers, the vector quantization technique, and the support vector machines. These methods suffer from a major disadvantage, namely, that of the excessive computational burden encountered by processing all the data. In this paper, we suggest a recursive and computationally superior mechanism referred to as adaptive recursive partitioning (ARP)_PRS. Rather than process all the data using a PRS, we propose that the data be recursively subdivided into smaller subsets. This recursive subdivision can be arbitrary, and need not utilize any underlying clustering philosophy. The advantage of ARP_PRS is that the PRS processes subsets of data points that effectively sample the entire space to yield smaller subsets of prototypes. These prototypes are then, in turn, gathered and processed by the PRS to yield more refined prototypes. In this manner, prototypes which are in the interior of the Voronoi spaces, and thus ineffective in the classification, are eliminated at the subsequent invocations of the PRS. We are unaware of any PRS that employs such a recursive philosophy. Although we marginally forfeit accuracy in return for computational efficiency, our experimental results demonstrate that the proposed recursive mechanism yields classification comparable to the best reported prototype condensation schemes reported to-date. Indeed, this is true for both artificial data sets and for samples involving real-life data sets. The results especially demonstrate that a fair computational advantage can be obtained by using such a recursive strategy for "large" data sets, such as those involved in data mining and text categorization applications.     

Enhancing prototype reduction schemes with LVQ3-type algorithms

Various prototype reduction schemes have been reported in the literature. Foremost among these are the prototypes for nearest neighbor (PNN), the vector quantization (VQ), and the support vector machines (SVM) methods. In this paper, we shall show that these schemes can be enhanced by the introduction of a post-processing phase that is related, but not identical to, the LVQ3 process. Although the post-processing with LVQ3 has been reported for the SOM and the basic VQ methods, in this paper, we shall show that an analogous philosophy can be used in conjunction with the SVM and PNN rules. Our essential modification to LVQ3 first entails a partitioning of the respective training sets into two sets called the Placement set and the Optimizing set, which are instrumental in determining the LVQ3 parameters. Such a partitioning is novel to the literature. Our experimental results demonstrate that the proposed enhancement yields the best reported prototype condensation scheme to-date for both artificial data sets, and for samples involving real-life data sets.     

Divergence-based classification in learning vector quantization

We discuss the use of divergences in dissimilarity-based classification. Divergences can be employed whenever vectorial data consists of non-negative, potentially normalized features. This is, for instance, the case in spectral data or histograms. In particular, we introduce and study divergence based learning vector quantization (DLVQ). We derive cost function based DLVQ schemes for the family of γ‐divergences which includes the well-known Kullback-Leibler divergence and the so-called Cauchy-Schwarz divergence as special cases. The corresponding training schemes are applied to two different real world data sets. The first one, a benchmark data set (Wisconsin Breast Cancer) is available in the public domain. In the second problem, color histograms of leaf images are used to detect the presence of cassava mosaic disease in cassava plants. We compare the use of standard Euclidean distances with DLVQ for different parameter settings. We show that DLVQ can yield superior classification accuracies and Receiver Operating Characteristics.     

全监督学习的图像语义分割方法研究进展

近年来,随着深度学习进入计算机视觉领域,各种深度学习图像语义分割方法相继出现,其中全监督学习方法的分割效果显著超过弱监督学习方法。将全监督学习的图像语义分割方法分为五类,并对各类中最具有代表性的方法进行详细分析,重点阐述各种方法核心部分的实现过程。对语义分割领域中的主流数据集进行归纳总结,介绍了性能算法指标,并在主流数据集上对各种代表性方法的效果进行对比,最后对语义分割的未来进行展望。     

基于主成分分析的SMO文本分类

利用SMO进行文本分类的核心问题是特征的选择问题,特征选择涉及到哪些特征和选择的特征维度问题。针对以上问题,介绍一种基于主成分分析和信息增益相结合的数据集样本降维的方法,并在此基础上对序贯最小优化算法进行改进,提出降维序贯最小优化(P-SOM)算法。P-SMO算法去掉了冗余维。实验结果证明,该方法提高SMO算法的性能,缩短支持向量机的训练时间,提高支持向量机的分类精度。     

支持向量机回归的参数选择方法

综合4种支持向量机回归的参数选择方法的优点,提出一种对训练样本进行分析并直接确定参数的方法。在标准测试数据集上的试验证明,该方法与传统网格搜索法相比,在时间和预测精度方面取得了更好的结果,可以较好地解决支持向量机在实际应用中参数难以选择、消耗时间长的问题。     

Analysing the power of deep learning techniques over the traditional methods using medicare utilisation and provider data

Deep Learning Technique (DLT) is the sub-branch of Machine Learning (ML) which assists to learn the data in multiple levels of representation and abstraction and shows impressive performance on many Artificial Intelligence (AI) tasks. This paper presents a new method to analyse the healthcare data using DLT algorithms and associated mathematical formulations. In this study, we have first developed a DLT to programme two types of deep learning neural networks, namely: (a) a two-hidden layer network, and (b) a three-hidden layer network. The data was analysed for predictability in both of these networks. Additionally, a comparison was also made with simple and multiple Linear Regression (LR). The demonstration of successful application of this method is carried out using the dataset that was constructed based on 2014 Medicare Provider Utilization and Payment Data. The results indicate a stronger case to use DLTs compared to traditional techniques like LR. Furthermore, it was identified that adding more hidden layers to neural network constructed for performing deep learning analysis did not have much impact on predictability for the dataset considered in this study. Therefore, the experimentation described in this article sets up a case for using DLTs over the traditional predictive analytics. The investigators assume that the algorithms described for deep learning is repeatable and can be applied for other types of predictive analysis on healthcare data. The observed results indicate, the accuracy obtained by DLT was 40% more accurate than the traditional multivariate LR analysis.     

Projection and bidirectional projection measures of single-valued neutrosophic sets and their decision-making method for mechanical design schemes

Abstract

Projection measure is one of important tools for handling decision-making problems. First, the paper proposes projection and bidirectional projection measures between single-valued neutrosophic sets, and then the comparison of numerical examples shows that the bidirectional projection measure is superior to the general projection measure in measuring closeness degree between two vectors. Next, we develop their decision-making method for selecting mechanical design schemes under a single-valued neutrosophic environment. Through the projection measure or bidirectional projection measure between each alternative and the ideal alternative with single-valued neutrosophic information, all the alternatives can be ranked and the best one can be selected as well. Finally, the proposed decision-making method is applied to the selection of design schemes of punching machine and its effectiveness and advantages are demonstrated by comparison with relative methods.