Intelligent Data Analysis - Volume 3, issue 1

Authors: Famili, A.

Article Type: Editorial

DOI: 10.3233/IDA-1999-3101

Citation: Intelligent Data Analysis, vol. 3, no. 1, pp. iii-iv, 1999

Authors: Gama, João | Brazdil, Pavel

Article Type: Research Article

Abstract: In this paper we present system Ltree for propositional supervised learning. Ltree is able to define decision surfaces both orthogonal and oblique to the axes defined by the attributes of the input space. This is done combining a decision tree with a linear discriminant by means of constructive induction. At each decision node Ltree defines a new instance space by insertion of new attributes that are projections of the examples that fall at this node over the hyper-planes given by a linear discriminant function. This new instance space is propagated down through the tree. Tests based on those new attributes …are oblique with respect to the original input space. Ltree is a probabilistic tree in the sense that it outputs a class probability distribution for each query example. The class probability distribution is computed at learning time, taking into account the different class distributions on the path from the root to the actual node. We have carried out experiments on twenty one benchmark datasets and compared our system with other well known decision tree systems (orthogonal and oblique) like C4.5, OC1, LMDT, and CART. On these datasets we have observed that our system has advantages in what concerns accuracy and learning times at statistically significant confidence levels. Show more

Keywords: Multivariate decision trees, Constructive induction, Machine learning

DOI: 10.3233/IDA-1999-3102

Citation: Intelligent Data Analysis, vol. 3, no. 1, pp. 1-22, 1999

Authors: Stärk, Katharina D.C. | Pfeiffer, Dirk U.

Article Type: Research Article

Abstract: Statistical classification problems are very common in veterinary epidemiology. Traditionally, parametric techniques such as logistic regression or discriminant analysis are used to analyse data sets that contain several classes of observations. However, characteristics of the data set such as high dimensionality, multicollinearity and non-homogeneity can make a data set unsuitable for parametric techniques. In this article, classification tree algorithms (ID3, C4.5, CHAID, CART) and artificial neural networks are suggested as non-parametric alternatives. Their application is illustrated using a field data set containing pig farms with 3 levels of respiratory disease prevalence. The performance of non-parametric classification algorithms is compared with …results from multinomial logistic regression. None of the algorithms was significantly better than the others. The proportions of correctly classified farms were between 84% and 96%. However, the data set was small (86 observations), which created technical problems when using the artificial neural networks and multinomial logistic regression. The choice of statistical technique should therefore be based on the objectives of the study and the data set under consideration. Classification trees are well-suited for exploratory data analysis. They are easy to apply and worth considering. Show more

Keywords: Classification trees, Artificial neural networks, Multinomial logistic regression

DOI: 10.3233/IDA-1999-3103

Citation: Intelligent Data Analysis, vol. 3, no. 1, pp. 23-35, 1999

Authors: Berthold, Michael R. | Huber, Klaus-Peter

Article Type: Research Article

Abstract: Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated to use or infeasible for high-dimensional problems. This article presents an efficient and easy to use algorithm to construct fuzzy graphs from example data. The resulting fuzzy graphs are based on locally independent fuzzy rules that operate solely on selected, important attributes. This enables the application of these fuzzy graphs also to problems in high dimensional spaces. Using illustrative examples and a real world data set …it is demonstrated how the resulting fuzzy graphs offer quick insights into the structure of the example data, that is, the underlying model. The underlying algorithm is demonstrated using several Java applets, which can be found under ‘Electronic annexes’ on www.elsevier.comilocate/ida. Show more

Keywords: Fuzzy Graphs, Learning, Rule Extraction, Function Approximation, Interpretation

DOI: 10.3233/IDA-1999-3104

Citation: Intelligent Data Analysis, vol. 3, no. 1, pp. 37-53, 1999

Authors: Yang, Jihoon | Parekh, Rajesh | Honavar, Vasant

Article Type: Research Article

Abstract: Multi-layer networks of threshold logic units (TLU) offer an attractive framework for the design of pattern classification systems. A new constructive neural network learning algorithm (DistAl) based on inter-pattern distance is introduced. DistAl constructs a single hidden layer of hyperspherical threshold neurons. Each neuron is designed to determine a cluster of training patterns belonging to the same class. The weights and thresholds of the hidden neurons are determined directly by comparing the inter-pattern distances of the training patterns. This offers a significant advantage over other constructive learning algorithms that use an iterative (and often time consuming) weight modification strategy to …train individual neurons. The individual clusters (represented by the hidden neurons) are combined by a single output layer of threshold neurons. The speed of DistAl makes it a good candidate for datamining and knowledge acquisition from large datasets. The paper presents results of experiments using several artificial and real-world datasets. The results demonstrate that DistAl compares favorably with other learning algorithms for pattern classification. Show more

Keywords: Neural networks, Constructive learning algorithms, Pattern classification

DOI: 10.3233/IDA-1999-3105

Citation: Intelligent Data Analysis, vol. 3, no. 1, pp. 55-73, 1999

Authors: Li, Han Lin | Yu, Jing Rung

Article Type: Research Article

Abstract: Most of the conventional regression methods can only estimate a piecewise polynomial function in which the exact positions or the probabilistic distribution of the change-points is prespecified. This paper proposes an optimization method to estimate a piecewise polynomial function with unknown change-points. We first express a piecewise function by the addition of some absolute terms. Utilizing the properties of this function, a piecewise regression model is then formulated to minimize the estimation errors subjected to an amount of change-points. The model is then solved by a modified goal programming technique, which is more computationally efficient than conventional goal programs. Numerical …examples demonstrate that the proposed method is very promising in estimating the piecewise regression with automatic change-point detection. Show more

Keywords: Change-point, Goal programming, Piecewise regression, Least absolute deviations, Robust regression

DOI: 10.3233/IDA-1999-3106

Citation: Intelligent Data Analysis, vol. 3, no. 1, pp. 75-85, 1999

Article Type: Calendar

DOI: 10.3233/IDA-1999-3107

Citation: Intelligent Data Analysis, vol. 3, no. 1, pp. 87-92, 1999