Greedy Algorithm for Inference of Decision Trees from Decision Rule Systems

• URL: http://arxiv.org/abs/2401.06793v1
• Date: Mon, 8 Jan 2024 09:28:55 GMT
• Title: Greedy Algorithm for Inference of Decision Trees from Decision Rule Systems
• Authors: Kerven Durdymyradov and Mikhail Moshkov
• Abstract summary: Decision trees and decision rule systems play important roles as attributes, knowledge representation tools, and algorithms. 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 time algorithm that simulates the operation of a decision tree on a given attribute.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Learning accurate and interpretable decision trees [27.203303726977616]
  We develop approaches to design decision tree learning algorithms given repeated access to data from the same domain. We study the sample complexity of tuning prior parameters in Bayesian decision tree learning, and extend our results to decision tree regression. We also study the interpretability of the learned decision trees and introduce a data-driven approach for optimizing the explainability versus accuracy trade-off using decision trees.
  arXiv  Detail & Related papers  (2024-05-24T20:10:10Z)
• An Interpretable Client Decision Tree Aggregation process for Federated Learning [7.8973037023478785]
  We propose an Interpretable Client Decision Tree aggregation process for Federated Learning scenarios. This model is based on aggregating multiple decision paths of the decision trees and can be used on different decision tree types, such as ID3 and CART. We carry out the experiments within four datasets, and the analysis shows that the tree built with the model improves the local models, and outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2024-04-03T06:53:56Z)
• Learning a Decision Tree Algorithm with Transformers [75.96920867382859]
  We introduce MetaTree, a transformer-based model trained via meta-learning to directly produce strong decision trees. We fit both greedy decision trees and globally optimized decision trees on a large number of datasets, and train MetaTree to produce only the trees that achieve strong generalization performance.
  arXiv  Detail & Related papers  (2024-02-06T07:40:53Z)
• TreeDQN: Learning to minimize Branch-and-Bound tree [78.52895577861327]
  Branch-and-Bound is a convenient approach to solving optimization tasks in the form of Mixed Linear Programs. The efficiency of the solver depends on the branchning used to select a variable for splitting. We propose a reinforcement learning method that can efficiently learn the branching.
  arXiv  Detail & Related papers  (2023-06-09T14:01:26Z)
• Construction of Decision Trees and Acyclic Decision Graphs from Decision Rule Systems [0.0]
  We study the complexity of constructing decision trees and acyclic decision graphs representing decision trees from decision rule systems. We discuss the possibility of not building the entire decision tree, but describing the computation path in this tree for the given input.
  arXiv  Detail & Related papers  (2023-05-02T18:40:48Z)
• Explainable Data-Driven Optimization: From Context to Decision and Back Again [76.84947521482631]
  Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. We introduce a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We demonstrate our approach by explaining key problems in operations management such as inventory management and routing.
  arXiv  Detail & Related papers  (2023-01-24T15:25:16Z)
• Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment [79.5678820246642]
  We show that certain action-value methods are more sample efficient than policy-gradient methods on transfer problems that require only sparse changes to a sequence of previously optimal decisions. We generalize the recently proposed societal decision-making framework as a more granular formalism than the Markov decision process.
  arXiv  Detail & Related papers  (2021-06-28T21:29:13Z)
• Decision Concept Lattice vs. Decision Trees and Random Forests [4.898744396854312]
  We merge the ideas underlying decision trees, their ensembles and FCA by proposing a new supervised machine learning model. Specifically, we first propose a state-time algorithm for constructing a part of the concept lattice that is based on a decision tree. Second, we describe a prediction scheme based on a concept lattice for solving both classification and regression problems.
  arXiv  Detail & Related papers  (2021-06-01T10:45:35Z)
• Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning [66.01622034708319]
  We propose a knowledge distillation based decision trees extension, dubbed rectified decision trees (ReDT) We extend the splitting criteria and the ending condition of the standard decision trees, which allows training with soft labels. We then train the ReDT based on the soft label distilled from a well-trained teacher model through a novel jackknife-based method.
  arXiv  Detail & Related papers  (2020-08-21T10:45:25Z)
• MurTree: Optimal Classification Trees via Dynamic Programming and Search [61.817059565926336]
  We present a novel algorithm for learning optimal classification trees based on dynamic programming and search. Our approach uses only a fraction of the time required by the state-of-the-art and can handle datasets with tens of thousands of instances.
  arXiv  Detail & Related papers  (2020-07-24T17:06:55Z)
• dtControl: Decision Tree Learning Algorithms for Controller Representation [0.0]
  Decision trees can be used to represent provably-correct controllers concisely. We present dtControl, an easily synthesised tool for representing memoryless controllers as decision trees.
  arXiv  Detail & Related papers  (2020-02-12T17:13:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.