An implementation of logical analysis of data

TLDR

LAD identifies minimal feature sets and hidden patterns that distinguish positive from negative outcome events. This paper introduces and implements a logic‑based methodology for analyzing observations, presenting numerical experiments that compare its classification performance to other approaches. The method combines discovered minimal feature sets and hidden patterns to build general classification procedures, and is applied in pilot studies on oil exploration, psychometric testing, and Chinese transitional economy analysis. Numerical experiments and pilot studies demonstrate that LAD achieves strong classification performance relative to other methods, while also offering flexible, case‑specific solutions across diverse domains.

Abstract

Describes a new, logic-based methodology for analyzing observations. The key features of this “logical analysis of data” (LAD) methodology are the discovery of minimal sets of features that are necessary for explaining all observations and the detection of hidden patterns in the data that are capable of distinguishing observations describing “positive” outcome events from “negative” outcome events. Combinations of such patterns are used for developing general classification procedures. An implementation of this methodology is described in this paper, along with the results of numerical experiments demonstrating the classification performance of LAD in comparison with the reported results of other procedures. In the final section, we describe three pilot studies on applications of LAD to oil exploration, psychometric testing and the analysis of developments in the Chinese transitional economy. These pilot studies demonstrate not only the classification power of LAD but also its flexibility and capability to provide solutions to various case-dependent problems.