Induction of Decision Trees based on Generalized Graph Queries

TL;DR

This paper introduces GGQ-ID3, a novel multi-relational decision tree algorithm that employs Generalized Graph Queries to incorporate complex, structured, and cyclic patterns in relational data for improved learning.

Contribution

The paper presents GGQ-ID3, a new decision tree method using dynamically built Generalized Graph Queries for multi-relational data, enabling complex pattern evaluation and structure-aware learning.

Findings

Successfully applied to real-world multi-relational datasets.

Generated interpretable classification trees with semantic patterns.

Maintained manageable complexity in multi-relational learning.

Abstract

Usually, decision tree induction algorithms are limited to work with non relational data. Given a record, they do not take into account other objects attributes even though they can provide valuable information for the learning task. In this paper we present GGQ-ID3, a multi-relational decision tree learning algorithm that uses Generalized Graph Queries (GGQ) as predicates in the decision nodes. GGQs allow to express complex patterns (including cycles) and they can be refined step-by-step. Also, they can evaluate structures (not only single records) and perform Regular Pattern Matching. GGQ are built dynamically (pattern mining) during the GGQ-ID3 tree construction process. We will show how to use GGQ-ID3 to perform multi-relational machine learning keeping complexity under control. Finally, some real examples of automatically obtained classification trees and semantic patterns are shown. ----- Normalmente, los algoritmos de inducci\'on de \'arboles de decisi\'on trabajan con datos no relacionales. Dado un registro, no tienen en cuenta los atributos de otros objetos a pesar de que \'estos pueden proporcionar informaci\'on \'util para la tarea de aprendizaje. En este art\'iculo presentamos GGQ-ID3, un algoritmo de aprendizaje de \'arboles de decisiones multi-relacional que utiliza Generalized Graph Queries (GGQ) como predicados en los nodos de decisi\'on. Los GGQs permiten expresar patrones complejos (incluyendo ciclos) y pueden ser refinados paso a paso. Adem\'as, pueden evaluar estructuras (no solo registros) y llevar a cabo Regular Pattern Matching. En GGQ-ID3, los GGQ son construidos din\'amicamente (pattern mining) durante el proceso de construcci\'on del \'arbol. Adem\'as, se muestran algunos ejemplos reales de \'arboles de clasificaci\'on multi-relacionales y patrones sem\'anticos obtenidos autom\'aticamente.