Intelligent Data Analysis: An International Journal

Among most of the existing procedures for mode detection of the underlying probability density function (pdf), preliminary to unsupervised statistical clustering, the ones that research modes as regions where the pdf is concave remain very interesting approaches. These techniques make use of a test that determines locally the convexity of the underlying pdf from the input patterns. However, the test area of sampling points may straddle a boundary between a convex region and a concave one, so that the assumptions for the test of the local convexity can be violated. Furthermore, this local test of convexity is very sensitive to details in the data structure and would rapidly become impracticable as the dimensionality of the data increases.

The present paper presents a new alternative based on the global convexity analysis instead the local convexity testing. A recursive separable hyperbolic filter, used as the principal tool for this proposed technique, is generalized to a multidimensional space. This filter is with a reliability criterion allowing to model as well the pdf variations as the noise attached to the density function.

Based on the characteristic theorem of convexity, the proposed technique assigns the concave label to modal regions and the convex label to valleys of the pdf according to an adequate hyperbolic filtering scheme. Modes are then extracted as concave connected components corresponding to the clusters in the mixture, and are used to assign the available observations to the clusters attached to them.

Experimental results, using real and artificially generated data sets with various complexities, demonstrate the effectiveness of the proposed method, which requires neither a starting classification, nor an a priori number of clusters or their distribution.

Most classification methods are based on the assumption that the data conforms to a stationary distribution. However, the real-world data is usually collected over certain periods of time, ranging from seconds to years, and ignoring possible changes in the underlying concept, also known as concept drift, may degrade the predictive performance of a classification model. Moreover, the computation time, the amount of required memory, and the model complexity may grow indefinitely with the continuous arrival of new training instances. This paper describes and evaluates OLIN, an online classification system, which dynamically adjusts the size of the training window and the number of new examples between model re-constructions to the current rate of concept drift. By using a fixed amount of computer resources, OLIN produces models, which have nearly the same accuracy as the ones that would be produced by periodically re-constructing the model from all accumulated instances. We evaluate the system performance on sample segments from two real-world streams of non-stationary data.

This paper introduces a strategy for training ensemble classifiers by analysing boosting within margin theory. We present a bound on the generalisation error of ensembled classifiers in terms of the 2-norm of the margin slack vector. We develop an effective, adaptive and robust boosting algorithm, DMBoost, by optimising this bound. The soft margin based quadratic loss function is insensitive to points having a large margin. The algorithm improves the generalisation performance of a system by ignoring the examples having small or negative margin.

We evaluate the efficacy of the proposed method by applying it to a text categorization task. Experimental results show that DMBoost performs significantly better than AdaBoost, hence validating the effectiveness of the method. Furthermore, experimental results on UCI data sets demonstrate that DMBoost generally outperforms AdaBoost.

This study proposes a piecewise nonlinear model based on the segmentation of financial time series. The basic concept of proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in the forecasting model. The proposed model consists of two stages. The first stage detects successive change points in time series dataset and forecasts change-point groups with backpropagation neural networks (BPNs). In this stage, the following three change-point detection methods are applied and compared: the parametric method, the nonparametric approach, and the model-based approach. The next stage forecasts the final output with BPN using the groups. This study applies the proposed model to interest rate forecasting and examines three different models based on various change point detection methods. The experimental result shows that the proposed models outperforms conventional neural network model.

A new PCA-based method for an optimal representation of multi-frequency polarimetric SAR images is proposed. The method performs the simultaneous diagonalization of the signal and multiplicative noise covariance matrices via one orthogonal matrix. The covariance matrix of the multiplicative noise becomes an identity matrix, which implies that the variance of the noise in each new image is unity, and is uncorrelated between transformed images. The covariance matrix of the SAR images is transformed to a diagonal matrix whose diagonal elements are ordered in decreasing value, which means that the new images are uncorrelated and will be ordered by their variances (qualities). The theoretical analysis and the implementation procedure of the method are given. The method has been applied on real SAR images. The compression ability of the method is proved via a reconstitution process of the original SAR images from a small number of new images with a minimal loss of information.