Our experiments with capital markets data suggest that the domain can be effectively modeled by classification rules induced from available historical data for the purpose of making gainful predictions for equityinvestments. New classification techniques developed at IBM Research, including minimal rule generation (R-MINI) and contextual feature analysis, seem robust enough for consistently extracting useful information from noisy domains such as financial markets. We will briefly introduce the rationale for our minimal rule generation technique, and the motivation for the use of contextual information in analyzing features. We will then describe our experience from several experiments with the S&P 500 data, illustrating the general methodology, and the results of correlations and simulated managed investment based on classification rules generated by R-MINI. Wewillsketchhow the rules for classifications can be effectively used for numerical prediction, and eventually to an investment ...

This paper describes the use of decision tree and rule induction in data mining applications. Of methods for classification and regression that have been developed in the fields of pattern recognition, statistics, and machine learning, these areofparticular interest for data mining since they utilize symbolic and interpretable representations. Symbolic solutions can provide a high degree of insight into the decision boundaries that exist in the data, and the logic underlying them. This aspect makes these predictive mining techniques particularly attractive in commercial and industrial data mining applications. We present hereasynopsis of some major state-of-the-art tree and rule mining methodologies, as well as some recent advances.

ion for Modeling and Prediction C. Apte, S.J. Hong, J. Lepre, S. Prasad, and B. Rosen IBM Research Division Technical Report RC-20271 RAMP: Rules Abstraction for Modeling and Prediction Chidanand Apte, Se June Hong, Jorge Lepre, Seema Prasad, and Barry Rosen IBM T.J. Watson Research Center Yorktown Heights, NY 10598 January 12, 1996 Abstract Generating accurate and robust models is crucial to the successful use and deployment of classifiers on a large scale. Rule induction, i.e., generating decision rule models from data, is often a preferred approach to classification modeling and prediction, due to the enhanced explanatory capability and interpretability of decision rules. The RAMP system for rules abstraction and modeling is evolving with accuracy and robustness as primary goals. The system provides the following key capabilities: 1) feature analysis and selection based upon contextual merits technique, 2) "optimal" discretization of numerical features, 3) generation of m...