DL-$elect: A Decision-List-Based Data-Mining System

Abstract

The application of machine-learning algorithms to the financial markets has been increasing in popularity in recent years. The majority of systems that have been created for the purpose of selecting stocks have utilized neural-network techniques. Our research has dealt with the feasibility of inductive logic approaches and the creation of a decision-list-based data-mining system, DL- $ lect. Neural networks can model a variety of data distributions and handle inconsistent data well. But for complex problems such as financial analysis, the structure of a neural network can be difficult to interpret. Decision lists (Rivest 1987), however, are represented in an easily understood form: an extended “if-then-elseif-...else- “ rule. Iterative algorithms for decision lists append rules into a list and remove examples from the data set that are covered