Intelligent Data Analysis - Volume 4, issue 5

Authors: Famili, A.

Article Type: Editorial

DOI: 10.3233/IDA-2000-4501

Citation: Intelligent Data Analysis, vol. 4, no. 5, pp. 395-395, 2000

Authors: Bursteinas, Borisas | Long, J.A.

Article Type: Research Article

Abstract: Tree-structured classifiers have proved their ability to show good result in comparison with other classification techniques applied to real-world data which is usually noisy and uncertain. The purpose of this article is to survey a representative selection of existing types of tree-structured classifiers and evaluate their abilities to classify data sets with and without highly correlated attributes. The primary focus, however, is on identifying the suitability of applying tree-structured algorithms to data with interconnected attributes which is an essential feature of financial and business data. To carry out this study two financial data sets are used. The first data set …contains quantitative data relating to a company's credit rating score. The second data set contains financial ratios related to company solvency. To determine the efficiency of different tree-structured algorithms five algorithms (four different types) were selected for comparison purposes. From the experimental results it is possible to see, that classification based on the mixed approach (NBTree) performed the best. Classifiers with a Bayesian approach also showed that they are stable. Show more

Keywords: decision tree, tree-structured classifiers, Bayesian classifiers, C5.0, OC1, feature selection, correlated features

DOI: 10.3233/IDA-2000-4502

Citation: Intelligent Data Analysis, vol. 4, no. 5, pp. 397-410, 2000

Authors: Calvo, Rafael A. | Ceccatto, H.A.

Article Type: Research Article

Abstract: In this work we investigate some technical questions related to the application of neural networks in document classification. First, we discuss the effects of different averaging protocols for the χ 2 statistic used to remove non-informative terms. This is an especially relevant issue for the neural network technique, which requires an aggressive dimensionality reduction to be feasible. Second, we estimate the importance of performance fluctuations due to inherent randomness in the training process of a neural network, a point not properly addressed in previous works. Finally, we compare the neural network results with those …obtained using the best methods for this application. For this we optimize the network architecture by evaluating much larger nets than previously considered in similar studies in the literature. Show more

Keywords: text classification, statistical learning, neural networks, knowledge management

DOI: 10.3233/IDA-2000-4503

Citation: Intelligent Data Analysis, vol. 4, no. 5, pp. 411-420, 2000

Authors: Yao, Yuhui | Chen, Yan Qiu | Chen, Lihui

Article Type: Research Article

Abstract: A novel similarity measure, proposed for clustering data with arbitrary distribution shapes, is developed. Such a new measure of similarity is employed in a dynamic model to collectively measure similarity among pattern vectors, which can help to achieve a more robust clustering performance than using the existing measures that are staticly and individually based on the distances among the isolated pairwise data. The experiment results demonstrated that the proposed neural network based on the new similarity measure has the capability to robustly and quickly cluster data on which Cluster-Detection-and-Labeling neural network fails.

Keywords: unsupervised learning, clustering, association clustering

DOI: 10.3233/IDA-2000-4504

Citation: Intelligent Data Analysis, vol. 4, no. 5, pp. 421-431, 2000

Authors: Tu, Te-Ming | Chen, Pa-Yuan

Article Type: Research Article

Abstract: In hyperspectral image analysis, determining a distinct material number is an important task for subsequent classification processes. Identifying the number of distinct materials is essentially the same task as determining the intrinsic dimensionality of the imaging spectrometer data. Minimum noise fraction (MNF) transformation or noise-adjusted principal component analysis (NAPCA) is a highly effective means of determining the inherent dimensionality of image data. However, inaccuracy in the noise estimation degrades the validity of this estimation. To effectively resolve this problem, this work presents a Noise-Adjusted Transformed Gerschgorin Disk approach (NATGD) which incorporates the NAPCA method into a transformed Gerschgorin disk (TGD) …approach. By noise-adjusted, Gerschgorin disks in NATGD can be formed into two distinct, non-overlapping collections; one for signals and the other for noises. Hence, the number of distinct materials can be visually determined by counting the number of Gerschgorin disks for signals. Experimental results demonstrate that the method proposed herein can effectively solve the intrinsic dimensionality problem. Show more

Keywords: noise-adjusted principal components analysis, noise-adjusted transformed Gerschgorin disk approach

DOI: 10.3233/IDA-2000-4505

Citation: Intelligent Data Analysis, vol. 4, no. 5, pp. 433-444, 2000

Authors: Bruha, Ivan | Kralik, Pavel | Berka, Petr

Article Type: Research Article

Abstract: Machine learning (ML) is a useful and productive component of data mining (DM). Given a large database, a learning algorithm induces a description of concepts (classes) which are immersed in a given problem area. The induction itself consists in searching usually a huge space of possible concept descriptions. There exist several paradigms for controlling this search. One of the promising and efficient paradigms are genetic algorithms (GAs). There have been done many research projects of incorporating genetic algorithms into the field of machine learning. This paper describes an efficient application of a GA in the attribute-based rule-inducing …learning algorithm. Actually, a domain-independent GA has been integrated into the covering learning algorithm CN4, a large extension of the well-known algorithm CN2; the induction procedure of CN4 (beam search methodology) has been removed and the GA has been implanted into this shell. Genetic algorithms are capable of processing symbolic attributes in a simple, natural manner. The processing of numerical (continuous) attributes by genetic algorithms is not so straightforward. One feasible strategy is to discretize numerical attributes before a generic algorithm is called. There exist quite a few discretization preprocessors in data mining and machine learning. This paper describes a newer preprocessor for discretization (categorization) of numerical attributes. The genuine discretization procedures generate sharp bounds (thresholds) between intervals. It may result in capturing training objects from various classes (concepts) into one interval that will not be `pure'; this in particular happens near the interval borders. One feasible way how to eliminate such an impurity around the interval borders is to fuzzify them. The paper first introduces the methodology of our new learning algorithm, the genetic learner. Then the discretization/fuzzification preprocessor is presented. Finally, the paper compares the entire system (a preprocessor and genetic learner) with well-known covering as well as TDIDT learning algorithms. Show more

Keywords: inductive learning, data mining, knowledge discovery in databases, genetic algorithms, numerical attributes, discretization, fuzzification

DOI: 10.3233/IDA-2000-4506

Citation: Intelligent Data Analysis, vol. 4, no. 5, pp. 445-460, 2000