. A brief overview of the history of the development of decision tree induction algorithms is followed by a review of techniques for dealing with missing attribute values in the operation of these methods. The technique of dynamic path generation is described in the context of treebased classification methods. The waste of data which can result from casewise deletion of missing values in statistical algorithms is discussed and alternatives proposed. Keywords: Missing values, Dynamic path generation, Intelligent data analysis, Inductive learning, Knowledge discovery, Data mining, Machine learning. 1 Introduction In the information age, data is generated almost everywhere: satellites orbiting the moons of Jupiter; submarines in the deepest ocean trench; even electronic point of sale machines in the high street produce data. All of these systems generate millions of megabytes of data every day. Some of these data contain information that could lead to important discoveries in science; s...

The problem of transforming the knowledge bases of expert systems using induced rules or decision trees into comprehensible knowledge structures is addressed. A knowledge structure is developed that generalizes and subsumes production rules, decision trees, and rules with exceptions. It gives rise to a natural complexity measure that allows them to be understood, analyzed and compared on a uniform basis. The structure is a directed acyclic graph with the semantics that nodes are premises, some of which have attached conclusions, and the arcs are inheritance links with disjunctive multiple inheritance. A detailed example is given of the generation of a range of such structures of equivalent performance for a simple problem, and the complexity measure of a particular structure is shown to relate to its perceived complexity. The simplest structures are generated by an algorithm that factors common sub-premises from the premises of rules. A more complex example of a chess dataset is used t...

This paper focusses on that form of learning which relates to exploration, rather than generalization. It uses the notion of exploration as the modification of state spaces within which search and decision making occur. It demonstrates that the genetic algorithm formalism provides a computational construct to carry out this learning. The process is exemplified using a shape grammar for a beam section. A new shape grammar is learned which produces a new state space for the problem. This new state space has improved characteristics.

A methodology for the modeling of large data sets is described which results in rule sets having minimal inter-rule interactions, and being simply maintained. An algorithm for developing such rule sets automatically is described and its efficacy shown with standard test data sets. Comparative studies of manual and automatic modeling of a data set of some nine thousand five hundred cases are reported. A study is reported in which ten years of patient data have been modeled on a month by month basis to determine how well a diagnostic system developed by automated induction would have performed had it been in use throughout the project.

. This paper introduces a new concept learning system. Its main features are presented and discussed. The controlled use of redundancy is one of the main characteristics of the program. Redundancy, in this system, is used to deal with several types of uncertainty existing in real domains. The problem of the use of redundancy is addressed, namely its influence on accuracy and comprehensibility. Extensive experiments were carried out on three real world domains. These experiments showed clearly the advantages of the use of redundancy. 1 Introduction This paper presents the learning system YAILS capable of obtaining high accuracy in noisy domains. One of the novel features of the program is its controlled use of redundancy. Several authors ([5, 7, 2]) reported experiments that clearly show an increase in accuracy when multiple sources of knowledge are used. On the other hand, the existence of redundancy decreases the comprehensibility of learned theories. The controlled use of redundancy...

The size and complexity of software and hardware systems have significantly increased in the past years. As a result, it is harder to guarantee their correct behavior. One of the most successful methods for automated verification of finite-state systems is model checking. Most of the current model-checking systems use binary decision diagrams (BDDs) for the representation of the tested model and in the verification process of its properties.

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Most deployed intrusion detection systems (IDSs) follow a signature-based approach where attacks are identified by matching each input event against predefined signatures that model malicious activity. This matching process

Abstract. Reinforcement Learning is a popular technique for gameplaying because it can learn an optimal policy for sequential decision problems in which the outcome (or reward) is delayed. However, Reinforcement Learning does not readily enable transfer of acquired knowledge to other instances. Case-Based Reasoning, in contrast, addresses exactly the issue of transferring solutions to slightly different instances of a problem. We describe a technique for abstracting reusable cases from Reinforcement Learning. We also report on preliminary experiments with case abstraction in a microworld. 1

With the widespread computerization in business, government, and science, the efficient and effective discovery of interesting information from large databases becomes essential. Data mining or Knowledge Discovery in Database (KDD) emerges as a solution to the data analysis problems faced by many organizations. Previous studies on data mining have been focused on the discovery of knowledge at a single conceptual level, either at the primitive level or at a rather high conceptual level. However, it is often desirable to discover knowledge at multiple conceptual levels, which will provide a spectrum of understanding, from general to specific, for the underlying data. In this thesis, we first introduce the conceptual hierarchy, a hierarchical organization of the data in the databases. Two algorithms for dynamic adjustment of conceptual hierarchies are developed, as well as another algorithm for automatic generation of conceptual hierarchies for numerical attributes. In addition, a set of ...