Classification of Multivariate Objects Using Interval Quantile Classes

The paper contains a proposal of interval data clustering related to given social and economic objects characterized by many interval variables. This multivariate approach is based on an original conception of interval quantiles constructed using a special definition derived from the notion of the Hausdorff distance. In order to improve the quality of classification, the obtained interval quantile classes can be next aggregated into larger merged classes. The efficiency of our method can be assessed using especially defined indices of entropy and volume coefficients. The second notion replaces the classical concept of area, which is not applicable in this case.     

A New Class of Weighted Similarity Indices Using Polytomous Variables

We introduce new similarity measures between two subjects, with reference to variables with multiple categories. In contrast to traditionally used similarity indices, they also take into account the frequency of the categories of each attribute in the sample. This feature is useful when dealing with rare categories, since it makes sense to differently evaluate the pairwise presence of a rare category from the pairwise presence of a widespread one. A weighting criterion for each category derived from Shannon??s information theory is suggested. There are two versions of the weighted index: one for independent categorical variables and one for dependent variables. The suitability of the proposed indices is shown in this paper using both simulated and real world data sets.     

Incremental Classification with Generalized Eigenvalues

Supervised learning techniques are widely accepted methods to analyze data for scientific and real world problems. Most of these problems require fast and continuous acquisition of data, which are to be used in training the learning system. Therefore, maintaining such systems updated may become cumbersome. Various techniques have been devised in the field of machine learning to solve this problem. In this study, we propose an algorithm to reduce the training data to a substantially small subset of the original training data to train a generalized eigenvalue classifier. The proposed method provides a constructive way to understand the influence of new training data on an existing classification function. We show through numerical experiments that this technique prevents the overfitting problem of the earlier generalized eigenvalue classifiers, while promising a comparable performance in classification with respect to the state-of-the-art classification methods.     

Classification Based on Depth Transvariations

Suppose y, a d-dimensional (d ≥ 1) vector, is drawn from a mixture of k (k ≥ 2) populations, given by ∏₁, ∏₂,…,∏ _k . We wish to identify the population that is the most likely source of the point y. To solve this classification problem many classification rules have been proposed in the literature. In this study, a new nonparametric classifier based on the transvariation probabilities of data depth is proposed. We compare the performance of the newly proposed nonparametric classifier with classical and maximum depth classifiers using some benchmark and simulated data sets. The authors thank the editor and referees for comments that led to an improvement of this paper. This work is partially supported by the National Science Foundation under Grant No. DMS-0604726. Published online xx, xx, xxxx.     

主题与分类

现代术语学的创始人欧根·维斯特(Eugen Wüster)的<普通术语学和术语词典编纂学导论>(Einfürung in die allgemeine Terminologie und terminologische Lexikographie)的德文版第三版(1991年在波恩出版) 中,讨论了术语学中的主题与分类的问题.笔者根据该书中"3.5,3.6, 3.7"的内容,写成了这篇读书札记.     

Classification of Asymmetric Proximity Data

When clustering asymmetric proximity data, only the average amounts are often considered by assuming that the asymmetry is due to noise. But when the asymmetry is structural, as typically may happen for exchange flows, migration data or confusion data, this may strongly affect the search for the groups because the directions of the exchanges are ignored and not integrated in the clustering process. The clustering model proposed here relies on the decomposition of the asymmetric dissimilarity matrix into symmetric and skew-symmetric effects both decomposed in within and between cluster effects. The classification structures used here are generally based on two different partitions of the objects fitted to the symmetric and the skew-symmetric part of the data, respectively; the restricted case is also presented where the partition fits jointly both of them allowing for clusters of objects similar with respect to the average amounts and directions of the data. Parsimonious models are presented which allow for effective and simple graphical representations of the results.     

Annual Conference of the Classification Society

Call for Abstracts

Annual Conference of the Classification Society     

Free Knot Splines for Supervised Classification

Data in many different fields come to practitioners through a process naturally described as functional. We propose a classification procedure of oxidation curves. Our algorithm is based on two stages: fitting the functional data by linear splines with free knots and classifying the estimated knots which estimate useful oxidation parameters. A real data set on 57 oxidation curves is used to illustrate our approach.     

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.     

医用内窥镜的概念与分类--以各专利分类体系为视角

在拥有海量专利文献的数据库中有效地利用分类号可以更便捷地获取所需信息,而各个分类号不同的细分特点也在一定程度上反映了世界主要经济地区在该领域的技术发展趋势。文章以当今各专利分类体系为视角,探讨医用内窥镜的概念及分类。     

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.     

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.