Robust Functional Supervised Classification for Time Series

We propose using the integrated periodogram to classify time series. The method assigns a new time series to the group that minimizes the distance between the series integrated periodogram and the group mean of integrated periodograms. Local computation of these periodograms allows the application of this approach to nonstationary time series. Since the integrated periodograms are curves, we apply functional data depth-based techniques to make the classification robust, which is a clear advantage over other competitive procedures. The method provides small error rates for both simulated and real data. It improves existing approaches and presents good computational behavior.     

Mining Supervised Classification Performance Studies: A Meta-Analytic Investigation

There have been many comparative studies of classification methods in which real datasets are used as a gauge to assess the relative performance of the methods. Since these comparisons often yield inconclusive or limited results on how methods perform, it is often believed that a broader approach combining these studies would shed some light on this difficult question. This paper describes such an attempt: we have sampled the available literature and created a dataset of 5807 classification results. We show that one of the possible ways to analyze the resulting data is an overall assessment of the classification methods, and we present methods for that particular aim. The merits and demerits of such an approach are discussed, and conclusions are drawn which may assist future research: we argue that the current state of the literature hardly allows large-scale investigations. This work was sponsored by the MOD Corporate Research Programme, CISP, as part of a larger project on technology assessment. We would like to express our appreciation to Andrew Webb for his support throughout the entire project, and to Wojtek Krzanowski for valuable comments on a draft of this paper. We would also like to thank the anonymous referees for some very interesting comments, some of which we hope to pursue in future work.     

Adaptive Mixture Discriminant Analysis for Supervised Learning with Unobserved Classes

In supervised learning, an important issue usually not taken into account by classical methods is that a class represented in the test set may have not been encountered earlier in the learning phase. Classical supervised algorithms will automatically label such observations as belonging to one of the known classes in the training set and will not be able to detect new classes. This work introduces a model-based discriminant analysis method, called adaptive mixture discriminant analysis (AMDA), which can detect several unobserved groups of points and can adapt the learned classifier to the new situation. Two EM-based procedures are proposed for parameter estimation and model selection criteria are used for selecting the actual number of classes. Experiments on artificial and real data demonstrate the ability of the proposed method to deal with complex and real-world problems. The proposed approach is also applied to the detection of unobserved communities in social network analysis.     

Default Priors for Neural Network Classification

Feedforward neural networks are a popular tool for classification, offering a method for fully flexible modeling. This paper looks at the underlying probability model, so as to understand statistically what is going on in order to facilitate an intelligent choice of prior for a fully Bayesian analysis. The parameters turn out to be difficult or impossible to interpret, and yet a coherent prior requires a quantification of this inherent uncertainty. Several approaches are discussed, including flat priors, Jeffreys priors and reference priors.     

Variable Selection for Clustering and Classification

As data sets continue to grow in size and complexity, effective and efficient techniques are needed to target important features in the variable space. Many of the variable selection techniques that are commonly used alongside clustering algorithms are based upon determining the best variable subspace according to model fitting in a stepwise manner. These techniques are often computationally intensive and can require extended periods of time to run; in fact, some are prohibitively computationally expensive for high-dimensional data. In this paper, a novel variable selection technique is introduced for use in clustering and classification analyses that is both intuitive and computationally efficient. We focus largely on applications in mixture model-based learning, but the technique could be adapted for use with various other clustering/classification methods. Our approach is illustrated on both simulated and real data, highlighted by contrasting its performance with that of other comparable variable selection techniques on the real data sets.     

Oscillation Heuristics for the Two-group Classification Problem

We propose a new nonparametric family of oscillation heuristics for improving linear classifiers in the two-group discriminant problem. The heuristics are motivated by the intuition that the classification accuracy of a separating hyperplane can be improved through small perturbations to its slope and position, accomplished by substituting training observations near the hyperplane for those used to generate it. In an extensive simulation study, using data generated from multivariate normal distributions under a variety of conditions, the oscillation heuristics consistently improve upon the classical linear and logistic discriminant functions, as well as two published linear programming-based heuristics and a linear Support Vector Machine. Added to any of the methods above, they approach, and frequently attain, the best possible accuracy on the training samples, as determined by a mixed-integer programming (MIP) model, at a much smaller computational cost. They also improve expected accuracy on the overall populations when the populations overlap significantly and the heuristics are trained with large samples, at least in situations where the data conditions do not explicitly favor a particular classifier.     

自由意志难题的语境消解

本文主要对自由意志难题提出一种语境论解决方案。近来对自由意志难题与怀疑论之间的讨论尤为引人注目。许多哲学家对怀疑论问题提出了语境主义解决方案。以此为据,本文将着重对自由意志难题和道德责任难题提出一种语境主义的解决方案。     

模块副现象论与自由意志危机

随着意志科学研究的不断深入,科学家们对意志实现的神经基础的物理构成、行为模式与活动机制有了愈加透彻的理解。Wegner采用现象学策略,区分出经验意志现象和现象意志现象,基于这两种现象的耦合程度,提出显明因果理论,结果把有意识意志视为没有真实因果作用的解释性虚构,提出模块副现象论。大多哲学家把模块副现象论视为科学对自由意志的严重威胁。除了从技术上批判李贝特行动实验有数据幻觉误导之嫌外,他们更多地是通过对自由意志的概念剖析来否认其科学证据的合法性。Tim认为人类的认知结构特点阻碍了我们对自由意志的把握,这一永恒的哲学命题缺乏认识论切入的可能性,只能存在于纯形而上学思辨之中,也不适用于科学研究。     

Design of Blurring Mean-Shift Algorithms for Data Classification

The mean-shift algorithm is an iterative method of mode seeking and data clustering based on the kernel density estimator. The blurring mean-shift is an accelerated version which uses the original data only in the first step, then re-smoothes previous estimates. It converges to local centroids, but may suffer from problems of asymptotic bias, which fundamentally depend on the design of its smoothing components. This paper develops nearest-neighbor implementations and data-driven techniques of bandwidth selection, which enhance the clustering performance of the blurring method. These solutions can be applied to the whole class of mean-shift algorithms, including the iterative local mean method. Extended simulation experiments and applications to well known data-sets show the goodness of the blurring estimator with respect to other algorithms.     

俄英医学同义术语的类型划分及英俄、汉俄医学词典编纂启示

同义现象是术语学界长期关注的热点问题,也是当代极具现实意义和有争议的问题。术语的同义现象是建立在表达同一个专业概念的不同术语单位基础上的一种语义关系。通过对医学各领域同义术语(主要是俄语和英语)的分析和描写,划分同义术语的类型,确定现代医学术语的特点,进而发现现代医学双语(主要是英俄、汉俄)词典中暂时还缺乏关于在术语系统功能化条件下使用某一同义术语的相关信息。因此,英俄、汉俄医学词典应对同义术语做相关的信息标注,以帮助使用者选取某一具体医学领域最适合的同义术语。     

知觉二元论与自由意志

本文以论文"知觉二元论:二元化的另一种方式"中所表述的知觉二元论为基础,来讨论意识研究中所面临的若干问题,如意识的起源和突现,自我意识与精神事件的关系,心灵存在的判别标准,他心问题,特别是自由意志与决定论的关系等问题。笔者认为,非决定论并不是自由意志的前提条件,化解自由意志与决定论的表面冲突是康德为我们设定的一个哲学理想。自由意志与主体知识的缺乏和心灵事件的内在知觉有关。单纯从外在主义的观点来看,自由意志是一个内在的幻象;然而人的内在知觉是真实存在的,从内在主义的观点来看我肯定能感觉到我的决断与行动能改变外在世界中事物进程并为此负责,这就是我们所称的自由意志。     

On Classification and Regression Trees for Multiple Responses and Its Application

In many application fields, multivariate approaches that simultaneously consider the correlation between responses are needed. The tree method can be extended to multivariate responses, such as repeated measure and longitudinal data, by modifying the split function so as to accommodate multiple responses. Recently, researchers have constructed some decision trees for multiple continuous longitudinal response and multiple binary responses using Mahalanobis distance and a generalized entropy index. However, these methods have limitations according to the type of response, that is, those that are only continuous or binary. In this paper, we will modify the tree for univariate response procedure and suggest a new tree-based method that can analyze any type of multiple responses by using GEE (generalized estimating equations) techniques. To compare the performance of trees, simulation studies on selection probability of true split variable will be shown. Finally, applications using epileptic seizure data and WWW data are introduced.     

A Probability for Classification Based on the Dirichlet Process Mixture Model

In this paper we provide an explicit probability distribution for classification purposes when observations are viewed on the real line and classifications are to be based on numerical orderings. The classification model is derived from a Bayesian nonparametric mixture of Dirichlet process model; with some modifications. The resulting approach then more closely resembles a classical hierarchical grouping rule in that it depends on sums of squares of neighboring values. The proposed probability model for classification relies on a numerical procedure based on a reversible Markov chain Monte Carlo (MCMC) algorithm for determining the probabilities. Some numerical illustrations comparing with alternative ideas for classification are provided.