Greedy Algorithm for Inference of Decision Trees from Decision Rule Systems
Decision trees and decision rule systems play important roles as classifiers, knowledge representation tools, and algorithms. They are easily interpretable models for data analysis, making them widely used and studied in computer science. Understanding the relationships between these two models is an important task in this field. There are well-known methods for converting decision trees into systems of decision rules. In this paper, we consider the inverse transformation problem, which is not so simple. Instead of constructing an entire decision tree, our study focuses on a greedy polynomial time algorithm that simulates the operation of a decision tree on a given tuple of attribute values.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeSimilar Papers 제목 키워드 기반
Efficient non-greedy optimization of decision trees
Decision trees and randomized forests are widely used in computer vision and machine learning. Standard algorithms for decision tree induction optimize the split functions one node at a time according to some splitting c…
Structured PredictionDilemma First Search for Effortless Optimization of NP-Hard Problems
To tackle the exponentiality associated with NP-hard problems, two paradigms have been proposed. First, Branch & Bound, like Dynamic Programming, achieve efficient exact inference but requires extensive information and a…
Alternating optimization of decision trees, with application to learning sparse oblique trees
Learning a decision tree from data is a difficult optimization problem. The most widespread algorithm in practice, dating to the 1980s, is based on a greedy growth of the tree structure by recursively splitting nodes, an…
Learning a Decision Tree Algorithm with Transformers
Decision trees are renowned for their ability to achieve high predictive performance while remaining interpretable, especially on tabular data. Traditionally, they are constructed through recursive algorithms, where they…
Meta-LearningA Reinforcement Learning Approach to Online Learning of Decision Trees
Online decision tree learning algorithms typically examine all features of a new data point to update model parameters. We propose a novel alternative, Reinforcement Learning- based Decision Trees (RLDT), that uses Reinf…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)