Intelligent Data Analysis: An International Journal

We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns.

The authors investigate a visualization framework for genetic algorithm, to express the evolutionary processes. Our framework differs from most existing methods in that it visualizes evolutionary processes from a population viewpoint, rather than chromosomal or problem spaces. A simple sexual selection model is used for demonstration purposes. We propose four visualization methods that are based on the framework. Those tools show how evolutionary trends and population characteristics are visually depicted. The framework is both user-friendly and extendable to other problems and models that address population changes over time.

Various predictive modeling approaches based on the customers' information may be used for selecting proper targets for a promoted product to entice customers into purchasers. However, there is a fundamental problem, the incomplete data which can yield biased results and deteriorate the accuracy of those approaches. So far, several methods such as case deletion and mean substitution are applied to handle the incomplete dataset in various domains. Those approaches are simple and easy to implement but may also provide biased results. Recently multiple imputation is suggested as a method to overcome the flaws in traditional treatments through reflecting the uncertainty of missing values in the incomplete dataset. This study is designed to introduce the multiple imputation technique and show two experimental works of several imputation methods applied to the real cases in electronic customer relationship management domain, the first with missing covariates and the second with missing targets. According to the results of the experimental works, the multiple-imputation based approaches produced the better performance than the traditional approaches in both of two case studies. Especially, the multiple imputation technique proved to be more effective in the dataset with a high missing rate than the one with a low missing rate.

In this paper the analysis and Data-Mining of a large data-set related to a very popular Italian Virtual Community is presented. The Community is constituted by more than half-million registered users, each characterized by a unique nickname and a personal "profile" filled during a registration procedure, on a voluntary basis. Two data-sets have been considered: the Data-Base of the Users (nicknames and profiles), and the log-file of the server hosting the Community web-site. This work is constituted by three main parts: 1) analysis and clustering of the User Data-Base; 2) sessionization of the log-file and clustering of the navigation session database; 3) correlation of User clusters and navigation session clusters. This analysis provides a complete and full-rounded picture of the Virtual Community.

In this paper we consider various methods for nonmetric multidimensional scaling. We focus on the nonmetric phase, for which we consider various alternatives: Kruskal's nonmetric phase, Guttman's nonmetric phase, monotone regression by monotone splines, and monotone regression by a monotone neural network. All methods are briefly described. We use sequential quadratic programming to estimate the weights of the neural network. An experimental comparison of the methods is given for various synthetic and real-life datasets. The monotone neural network performs comparable to the traditional methods.

In this paper we provide a comprehensive empirical review of a variant of the Recursive Naïve Baye Classifier (RNBC*) in comparison to simple Naïve Bayes and C4.5. We show that in terms of a zero one loss cost function for classification accuracy, RNBC* outperformed Naïve Bayes and was comparable to C4.5, for the range of data-sets tested. As the Naïve Bayes classifier has been shown to be a robust classifier in many domains, this is a significant result. We estimate the bias variance decomposition of RNBC* and show that the bias-variance profile of RNBC* is more similar to that of decision trees than Naïve Bayes. We demonstrate how variance reducing ensemble techniques such as Bagging and Boosting can be effective in increasing the classification accuracy of RNBC*.