Usama M. Fayyad

On the Handling of Continuous-Valued Attributes in Decision Tree Generation

We present a result applicable to classification learning algorithms that generate decision trees or rules using the information entropy minimization heuristic for discretizing continuous-valued attributes. The result serves to give a better understanding of the entropy measure, to point out that the behavior of the information entropy heuristic possesses desirable properties that justify its usage in a formal sense, and to improve the efficiency of evaluating continuous-valued attributes for cut value selection. Along with the formal proof, we present empirical results that demonstrate the theoretically expected reduction in evaluation effort for training data sets from real-world domains.

Improved decision trees: a generalized version of ID3

Abstract A popular and particularly efficient method for inducing classification rules from examples is Quinlans ID3 algorithm. This paper examines the problem of overspecialization in ID3. Two causes of overspecialization in ID3 are identified. An algorithm that avoids some of the inherent problems in ID3 is developed. The new algorithm, GID3, is applied to the development of an expert system for automating the Reactive Ion Etching (RIE) process in semiconductor manufacturing. Six performance measures for decision trees are defined. The GID3 algorithm is empirically shown to outperform ID3 on all performance measures considered. The improvement is gained with negligible increase in computational complexity.

Application of machine learning techniques to semiconductor manufacturing

The advancement of VLSI technology has reached the stage where the automation of semiconductor manufacturing has become imminent. A natural step towards this end is to apply the available expert system technology to the task of intelligent control, monitoring and diagnosis of various processes and equipment for the IC manufacturing environment. This paper gives an overview of a machine learning program (GID3) and its use in automating the knowledge acquisition needed for the construction of an expert system for controlling the Reactive Ion Etching (RIE) process in IC manufacturing. We argue the appropriateness and necessity of machine learning to circumvent the “knowledge acquisition bottleneck”. We then motivate and describe the learning algorithm we developed. The GID3 system was applied to five different projects with several SRC industrial institutions. We describe some of the application areas where an acceptable level of success was achieved by the program. The application areas include: identification of relationships between RIE process anomalies and the corresponding parameter settings, acquiring a set of rules for correcting RIE process parameters contributing to abnormal output, and knowledge acquisition for an emitter piloting advisory expert system. The main theme of this paper is to bring attention to machine learning as a useful tool in the automation of the IC manufacturing process and as an aid to engineers in interpreting and assimilating experimental results.