CAQ: A machine learning tool for engineering

Abstract Machine learning algorithms developed from artificial intelligence (AI) research are rapidly finding useful applications in engineering domains. They are joining a family of tools that can help engineers summarize (or synthesize) massive amounts of data to support decision making. Traditional tools in this family, such as various statistical-based techniques, deal mostly with continuous (real) valued attributes — their results are relatively accurate, but often hard to interpret. On the other hand, logic-based learning tools, such as Michalski's AQ series of programs for learning concepts from examples, deal only with discrete valued attributes — their results are easier to comprehend, but not accurate for engineering applications which rarely deal solely with discrete valued attributes. Ideally, engineers need algorithms that work with a mix of attributes including those that take continuous values and those that take discrete values. Engineering problems also demand that learning algorithms be able to handle noise and produce concepts that are comprehensible. CAQ (Continuous AQ) is an algorithm designed to meet engineering requirements. This paper explains how CAQ combines continuous and discrete values, and shows that handling continuous attributes as real numbers instead of forcing them into a discrete representation leads to more efficient concept formation. Data from a turning process simulator used in support of machine operation planning in manufacturing are used to demonstrate the new algorithm.

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