Bayes Point Rule Set Learning

• URL: http://arxiv.org/abs/2204.05251v1
• Date: Mon, 11 Apr 2022 16:50:41 GMT
• Title: Bayes Point Rule Set Learning
• Authors: Fabio Aiolli, Luca Bergamin, Tommaso Carraro, Mirko Polato
• Abstract summary: Interpretability is having an increasingly important role in the design of machine learning algorithms. Disjunctive Normal Forms are arguably the most interpretable way to express a set of rules. We propose an effective bottom-up extension of the popular FIND-S algorithm to learn DNF-type rulesets.
• Score: 5.065947993017157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interpretability is having an increasingly important role in the design of machine learning algorithms. However, interpretable methods tend to be less accurate than their black-box counterparts. Among others, DNFs (Disjunctive Normal Forms) are arguably the most interpretable way to express a set of rules. In this paper, we propose an effective bottom-up extension of the popular FIND-S algorithm to learn DNF-type rulesets. The algorithm greedily finds a partition of the positive examples. The produced DNF is a set of conjunctive rules, each corresponding to the most specific rule consistent with a part of positive and all negative examples. We also propose two principled extensions of this method, approximating the Bayes Optimal Classifier by aggregating DNF decision rules. Finally, we provide a methodology to significantly improve the explainability of the learned rules while retaining their generalization capabilities. An extensive comparison with state-of-the-art symbolic and statistical methods on several benchmark data sets shows that our proposal provides an excellent balance between explainability and accuracy.

Related papers

• Obtaining Explainable Classification Models using Distributionally Robust Optimization [12.511155426574563]
  We study generalized linear models constructed using sets of feature value rules. An inherent trade-off exists between rule set sparsity and its prediction accuracy. We propose a new formulation to learn an ensemble of rule sets that simultaneously addresses these competing factors.
  arXiv  Detail & Related papers  (2023-11-03T15:45:34Z)
• Boosting Fair Classifier Generalization through Adaptive Priority Reweighing [59.801444556074394]
  A performance-promising fair algorithm with better generalizability is needed. This paper proposes a novel adaptive reweighing method to eliminate the impact of the distribution shifts between training and test data on model generalizability.
  arXiv  Detail & Related papers  (2023-09-15T13:04:55Z)
• ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning [107.61997887260056]
  We propose a novel framework, ChatRule, unleashing the power of large language models for mining logical rules over knowledge graphs. Specifically, the framework is initiated with an LLM-based rule generator, leveraging both the semantic and structural information of KGs. To refine the generated rules, a rule ranking module estimates the rule quality by incorporating facts from existing KGs.
  arXiv  Detail & Related papers  (2023-09-04T11:38:02Z)
• Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits [85.27420062094086]
  Implicitly Normalized Forecaster (INF) is considered an optimal solution for adversarial multi-armed bandit (MAB) problems. We propose a new version of INF called the Implicitly Normalized Forecaster with clipping (INFclip) for MAB problems with heavy-tailed settings. We demonstrate that INFclip is optimal for linear heavy-tailed MAB problems and works well for non-linear ones.
  arXiv  Detail & Related papers  (2023-05-11T12:00:43Z)
• Efficient learning of large sets of locally optimal classification rules [0.0]
  Conventional rule learning algorithms aim at finding a set of simple rules, where each rule covers as many examples as possible. In this paper, we argue that the rules found in this way may not be the optimal explanations for each of the examples they cover. We propose an efficient algorithm that aims at finding the best rule covering each training example in a greedy optimization consisting of one specialization and one generalization loop.
  arXiv  Detail & Related papers  (2023-01-24T11:40:28Z)
• Machine Learning with Probabilistic Law Discovery: A Concise Introduction [77.34726150561087]
  Probabilistic Law Discovery (PLD) is a logic based Machine Learning method, which implements a variant of probabilistic rule learning. PLD is close to Decision Tree/Random Forest methods, but it differs significantly in how relevant rules are defined. This paper outlines the main principles of PLD, highlight its benefits and limitations and provide some application guidelines.
  arXiv  Detail & Related papers  (2022-12-22T17:40:13Z)
• RulE: Knowledge Graph Reasoning with Rule Embedding [69.31451649090661]
  We propose a principled framework called textbfRulE (stands for Rule Embedding) to leverage logical rules to enhance KG reasoning. RulE learns rule embeddings from existing triplets and first-order rules by jointly representing textbfentities, textbfrelations and textbflogical rules in a unified embedding space. Results on multiple benchmarks reveal that our model outperforms the majority of existing embedding-based and rule-based approaches.
  arXiv  Detail & Related papers  (2022-10-24T06:47:13Z)
• Interpretable and Fair Boolean Rule Sets via Column Generation [18.08486863429421]
  An integer program is formulated to optimally trade classification accuracy for rule simplicity. We consider the fairness setting and extend the formulation to include explicit constraints on two different measures of classification parity. Compared to other fair and interpretable classifiers, our method is able to find rule sets that meet stricter notions of fairness with a modest trade-off in accuracy.
  arXiv  Detail & Related papers  (2021-11-16T13:40:28Z)
• How Fine-Tuning Allows for Effective Meta-Learning [50.17896588738377]
  We present a theoretical framework for analyzing representations derived from a MAML-like algorithm. We provide risk bounds on the best predictor found by fine-tuning via gradient descent, demonstrating that the algorithm can provably leverage the shared structure. This separation result underscores the benefit of fine-tuning-based methods, such as MAML, over methods with "frozen representation" objectives in few-shot learning.
  arXiv  Detail & Related papers  (2021-05-05T17:56:00Z)
• Better Short than Greedy: Interpretable Models through Optimal Rule Boosting [10.938624307941197]
  Rule ensembles are designed to provide a useful trade-off between predictive accuracy and model interpretability. We present a novel approach aiming to fit rule ensembles of maximal predictive power for a given ensemble size.
  arXiv  Detail & Related papers  (2021-01-21T01:03:48Z)
• Towards Learning Instantiated Logical Rules from Knowledge Graphs [20.251630903853016]
  We present GPFL, a probabilistic learner rule optimized to mine instantiated first-order logic rules from knowledge graphs. GPFL utilizes a novel two-stage rule generation mechanism that first generalizes extracted paths into templates that are acyclic abstract rules. We reveal the presence of overfitting rules, their impact on the predictive performance, and the effectiveness of a simple validation method filtering out overfitting rules.
  arXiv  Detail & Related papers  (2020-03-13T00:32:46Z)

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.