systems Abstract. The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions. 1.

this article. 0738-4602/92/$4.00 1992 AAAI 58 AI MAGAZINE for the 1990s (Silberschatz, Stonebraker, and Ullman 1990)

Abstract not found

This article presents an incremental algorithm for inducing decision trees equivalent to those formed by Quinlan's nonincremental ID3 algorithm, given the same training instances. The new algorithm, named ID5R, lets one apply the ID3 induction process to learning tasks in which training instances are presented serially.

. One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments. Keywords: fraud detection, rule l...

Genetic algorithms represent a class of adaptive search techniques that have been intensively studied in recent years, Much of the interest in genetic algorithms is due to the fact that they provide a set of efficient domain-independent search heuristics which are a significant improvement over traditional "weak methods" without the need for incorporating highly domain-specific knowledge. There is now considerable evidence that genetic algorithms are useful for global function optimization and NP-hurd problems. Recently, there has been a good deal of interest in using genetic algorithms for machine learning problems. This paper provides a brief overview of how one might use genetic algorithms as a key element in learning systems.

When Newell introduced the concept of the knowledge level as a useful level of description for computer systems, he focused on the representation of knowledge. This paper applies the knowledge level notion to the problem of knowledge acquisition. Two interesting issues arise. First, some existing machine learning programs appear to be completely static when viewed at the knowledge level. These programs improve their performance without changing their "knowledge." Second, the behavior of some other machine learning programs cannot be predicted or described at the knowledge level. These programs take unjustified inductive leaps. The first programs are called symbol level learning (SLL) programs; the second, non-deductive knowledge level learning (NKLL) programs. The paper analyzes both of these classes of learning programs and speculates on the possibility of developing coherent theories of each. A theory of symbol level learning is sketched, and some reasons are presented for believing...

A tight integration of Mitchell's version space algorithm with Agrawal et al.'s Apriori algorithm is presented. The algorithm can be used to generate patterns that satisfy a variety of constraints on data. Constraints that can be impoesed on...

Abstract not found

Solutions to the problem of learning from examples will have far-reaching benefits, and therefore, the problem is one of the most widely studied in the field of machine learning. The purpose of this study is to investigate a general solution method for the problem, the automatic generation of decision (or classification) trees. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for one problem domain, software resource data analysis. The purpose of the decision trees is to identify classes of objects (software modules) that had high development effort or faults, where "high" was defined to be in the uppermost quartile relative to past data. Sixteen software systems ranging from 3000 to 112,000 source lines have been selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4700 objects, capture a multitude of information about the objects: development effort...