Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming
- URL: http://arxiv.org/abs/2410.11000v1
- Date: Mon, 14 Oct 2024 18:32:29 GMT
- Title: Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming
- Authors: Akihiro Takemura, Katsumi Inoue,
- Abstract summary: We propose a method for generating rule sets as global and local explanations for tree-ensemble learning methods.
For global explanations, candidate rules are chosen from the entire trained tree-ensemble models.
For local explanations, candidate rules are selected by only considering rules that are relevant to the particular predicted instance.
- Score: 4.820391833117535
- License:
- Abstract: We propose a method for generating rule sets as global and local explanations for tree-ensemble learning methods using Answer Set Programming (ASP). To this end, we adopt a decompositional approach where the split structures of the base decision trees are exploited in the construction of rules, which in turn are assessed using pattern mining methods encoded in ASP to extract explanatory rules. For global explanations, candidate rules are chosen from the entire trained tree-ensemble models, whereas for local explanations, candidate rules are selected by only considering rules that are relevant to the particular predicted instance. We show how user-defined constraints and preferences can be represented declaratively in ASP to allow for transparent and flexible rule set generation, and how rules can be used as explanations to help the user better understand the models. Experimental evaluation with real-world datasets and popular tree-ensemble algorithms demonstrates that our approach is applicable to a wide range of classification tasks. Under consideration in Theory and Practice of Logic Programming (TPLP).
Related papers
- Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts [14.76420070558434]
Rule extrapolation describes OOD scenarios, where the prompt violates at least one rule.
We focus on formal languages, which are defined by the intersection of rules.
We lay the first stones of a normative theory of rule extrapolation, inspired by the Solomonoff prior in algorithmic information theory.
arXiv Detail & Related papers (2024-09-09T22:36:35Z) - Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning [53.241569810013836]
We propose a new framework based on large language models (LLMs) and decision Tree reasoning (OCTree)
Our key idea is to leverage LLMs' reasoning capabilities to find good feature generation rules without manually specifying the search space.
Our empirical results demonstrate that this simple framework consistently enhances the performance of various prediction models.
arXiv Detail & Related papers (2024-06-12T08:31:34Z) - Counterfactual Metarules for Local and Global Recourse [19.566362530518717]
T-CREx is a model-agnostic method for local and global counterfactual explanation.
It summarises recourse options for both individuals and groups in the form of human-readable rules.
arXiv Detail & Related papers (2024-05-29T08:35:17Z) - Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs [87.34281749422756]
Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks.
However, their mastery of underlying inferential rules still falls short of human capabilities.
We propose a logic scaffolding inferential rule generation framework, to construct an inferential rule base, ULogic.
arXiv Detail & Related papers (2024-02-18T03:38:51Z) - Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by
Answer Set Programming [9.221315229933532]
We propose a method for generating explainable rule sets from tree-ensemble learners using Answer Set Programming (ASP)
We adopt a decompositional approach where the split structures of the base decision trees are exploited in the construction of rules.
We show how user-defined constraints and preferences can be represented declaratively in ASP to allow for transparent and flexible rule set generation.
arXiv Detail & Related papers (2021-09-17T01:47:38Z) - On the Foundations of Grounding in Answer Set Programming [4.389457090443418]
We provide a comprehensive elaboration of the theoretical foundations of variable instantiation, or grounding, in Answer Set Programming (ASP)
We introduce a formal characterization of grounding algorithms in terms of (fixed point) operators.
A major role is played by dedicated well-founded operators whose associated models provide semantic guidance for delineating the result of grounding.
arXiv Detail & Related papers (2021-08-10T16:23:49Z) - Structural Learning of Probabilistic Sentential Decision Diagrams under
Partial Closed-World Assumption [127.439030701253]
Probabilistic sentential decision diagrams are a class of structured-decomposable circuits.
We propose a new scheme based on a partial closed-world assumption: data implicitly provide the logical base of the circuit.
Preliminary experiments show that the proposed approach might properly fit training data, and generalize well to test data, provided that these remain consistent with the underlying logical base.
arXiv Detail & Related papers (2021-07-26T12:01:56Z) - Rule Generation for Classification: Scalability, Interpretability, and Fairness [0.0]
We propose a new rule-based optimization method for classification with constraints.
We address interpretability and fairness by assigning cost coefficients to the rules and introducing additional constraints.
The proposed method exhibits a good compromise between local interpretability and fairness on the one side, and accuracy on the other side.
arXiv Detail & Related papers (2021-04-21T20:31:28Z) - An Integer Linear Programming Framework for Mining Constraints from Data [81.60135973848125]
We present a general framework for mining constraints from data.
In particular, we consider the inference in structured output prediction as an integer linear programming (ILP) problem.
We show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules.
arXiv Detail & Related papers (2020-06-18T20:09:53Z) - Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies [76.83991682238666]
Branch and Bound (B&B) is the exact tree search method typically used to solve Mixed-Integer Linear Programming problems (MILPs)
We propose a novel imitation learning framework, and introduce new input features and architectures to represent branching.
arXiv Detail & Related papers (2020-02-12T17:43:23Z) - Hierarchical Variational Imitation Learning of Control Programs [131.7671843857375]
We propose a variational inference method for imitation learning of a control policy represented by parametrized hierarchical procedures (PHP)
Our method discovers the hierarchical structure in a dataset of observation-action traces of teacher demonstrations, by learning an approximate posterior distribution over the latent sequence of procedure calls and terminations.
We demonstrate a novel benefit of variational inference in the context of hierarchical imitation learning: in decomposing the policy into simpler procedures, inference can leverage acausal information that is unused by other methods.
arXiv Detail & Related papers (2019-12-29T08:57:02Z)
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.