Adding value to induced decision trees for time-sensitive data

Rule induction serves as an alternative knowledge acquisition method in the development of expert systems. This research presents ways to increase the information in induced decision trees, which convert to rules. Common rule induction methods do not organize data by time; however, time is a factor in many common induction applications, such as bankruptcy prediction and credit evaluation. Utilizing the time sequences in data can result in a more valuable decision support tool. This article extends rule induction algorithms to handle such data. It also illustrates the value of staged induction for a new real-world application: predicting hospital patients’ length of stay. In the hospital application, the goal is to identify, as early as possible, patients likely to have an excessive length of stay, based on data gathered at admission and data that arrive gradually during the hospitalization period. In the context of this application, this article attempts to resolve conflicting results in the literature in two areas: 1) selecting attributes by the improvement in classification cost which they produce, and 2) stopping versus pruning in tree development. One conclusion is that selection and pruning by cost do not increase the average value of induced trees in this application.