Collapsing the Decision Tree: the Concurrent Data Predictor

TL;DR

This paper introduces a novel concurrent data predictor derived from decision trees by evaluating attributes simultaneously, resulting in a flat structure that improves prediction accuracy.

Contribution

It presents a new approach to decision tree prediction by removing sequential evaluation, enabling concurrent attribute assessment and enhancing accuracy.

Findings

Improved prediction accuracy over traditional decision trees

Flat structure reduces evaluation complexity

Concurrent evaluation speeds up prediction process

Abstract

A family of concurrent data predictors is derived from the decision tree classifier by removing the limitation of sequentially evaluating attributes. By evaluating attributes concurrently, the decision tree collapses into a flat structure. Experiments indicate improvements of the prediction accuracy.