Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach to this objective is to apply various machine learning algorithms to compute descriptive models of the available data. Here, we explore one of the main challenges in this research area, the development of techniques that scale up to large and possibly physically distributed databases. Meta-learning is a technique that seeks to compute higher-level classifiers (or classification models), called meta-classifiers, that integrate in some principled fashion multiple classifiers computed separately over different databases. This study, describes meta-learning and presents the JAM system (Java Agents for Meta-learning), an agent-based meta-learning system for large-scale data mining applications. Specifically, it identifies and addresses several important desiderata for distributed data mining systems that stem from their additional complexity co...

Abstract not found

This paper proposes a method to improve ID5R, an incremental TDIDT algorithm. The new method evaluates the quality of attributes selected at the nodes of a decision tree and estimates a minimum number of steps for which these attributes are guaranteed such a selection. This results in reducing overheads during incremental learning. The method is supported by theoretical analysis and experimental results. Keywords: Incremental algorithm, decision tree induction 1.

This paper extends the decision tree technique to an uncertain environment where the uncertainty is represented by belief functions as interpreted in the Transferable Belief Model (TBM). This so-called belief decision tree is a new classification method adapted to uncertain data. We will be concerned with the construction of the belief decision tree from a training set where the knowledge about the instances' classes is represented by belief functions, and its use for the classification of new instances where the knowledge about the attributes' values is represented by belief functions. Keywords: Belief Functions, Decision Tree, Belief Decision Tree, Classification, Transferable Belief Model. 1 Introduction Several learning methods have been developed to ensure classification. Among these, the decision tree method may be one of the most commonly used in supervised learning approaches. Indeed decision trees are characterized by their capability to break down a complex decision problem ...

Artificial intelligence research has had great success in many clasic games such as chess, checkers, and othello. In these perfect-information domains, alpha-beta search is sufficient to achieve a high level of play. However Artificial intelligence research has long been criticized for focusing on deterministic domains of perfect information -- many problems in the real world exhibit properties of imperfect or incomplete information and non-determinism. Poker, the archetypal game studied by...

Abstract not found

sdyna is a general framework designed to address large stochastic reinforcement learning (rl) problems. Unlike previous model-based methods in Factored mdps (fmdps), it incrementally learns the structure of a rl problem using supervised learning techniques. spiti is an instantiation of sdyna that uses decision trees as factored representations. First, we show that, in structured rl problems, spiti learns the structure of fmdps using Chi-Square tests and performs better than classical tabular model-based methods. Second, we study the generalization property of spiti using a Chi-Square based measure of the accuracy of the model built by spiti. 1

Classification is an important, well-known problem in the field of data mining, and has been studied extensively by several research communities. Thanks to the advances in data collection technologies and large scale business enterprises, the datasets for data mining applications are usually large and may involve several millions of records with high dimensionality, which make the task of classification computationally very expensive. In addition, rapid growth of data can continuously make the previously constructed classi er obsolete. In this paper, we propose a framework named ICE for incrementally classifying such ever-growing large datasets. The framework is scalable with minimal data access requirements, and can be inherently migrated to parallel as well as distributed computing environments easily. We provide mathematical background for incremental classification based on weighted samples and sampling techniques that extract weighted samples from decision trees. Experimental...

This paper describes the design and analysis of a system developed to learn comprehensible theories from structured data. The underlying knowledge representation formalism is a polymorphically-typed higher-order logic. To model structured data, a class of terms suitable for representing a wide range of data is identified. To encode structural boolean features, a class of predicates that can be built up by composition of simple functions is introduced. For any particular application, we give a mechanism to define and enumerate a set of relevant predicates. To construct comprehensible theories, we adopt the family of decision-tree algorithms, which include the standard top-down induction algorithm for learning decision trees, and the covering algorithm for learning decision lists. We show how these algorithms can be extended to work with structured data and complex predicates in both the batch and on-line settings. Using classical learning-theoretic results, we analyse the generalisation performance of our algorithms. The utility of the system is demonstrated with applications in the areas of bioinformatics and intelligent agents.