FAIR: Filtering of Automatically Induced Rules

• URL: http://arxiv.org/abs/2402.15472v2
• Date: Thu, 4 Jul 2024 07:06:22 GMT
• Title: FAIR: Filtering of Automatically Induced Rules
• Authors: Divya Jyoti Bajpai, Ayush Maheshwari, Manjesh Kumar Hanawal, Ganesh Ramakrishnan,
• Abstract summary: We propose an algorithm to filter rules from a large number of automatically induced rules. We show that our algorithm achieves statistically significant results in comparison to existing rule-filtering approaches.
• Score: 29.777290150010504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The availability of large annotated data can be a critical bottleneck in training machine learning algorithms successfully, especially when applied to diverse domains. Weak supervision offers a promising alternative by accelerating the creation of labeled training data using domain-specific rules. However, it requires users to write a diverse set of high-quality rules to assign labels to the unlabeled data. Automatic Rule Induction (ARI) approaches circumvent this problem by automatically creating rules from features on a small labeled set and filtering a final set of rules from them. In the ARI approach, the crucial step is to filter out a set of a high-quality useful subset of rules from the large set of automatically created rules. In this paper, we propose an algorithm (Filtering of Automatically Induced Rules) to filter rules from a large number of automatically induced rules using submodular objective functions that account for the collective precision, coverage, and conflicts of the rule set. We experiment with three ARI approaches and five text classification datasets to validate the superior performance of our algorithm with respect to several semi-supervised label aggregation approaches. Further, we show that achieves statistically significant results in comparison to existing rule-filtering approaches.

Related papers

• RulePrompt: Weakly Supervised Text Classification with Prompting PLMs and Self-Iterative Logical Rules [30.239044569301534]
  Weakly supervised text classification (WSTC) has attracted increasing attention due to its applicability in classifying a mass of texts. We propose a prompting PLM-based approach named RulePrompt for the WSTC task, consisting of a rule mining module and a rule-enhanced pseudo label generation module. Our approach yields interpretable category rules, proving its advantage in disambiguating easily-confused categories.
  arXiv  Detail & Related papers  (2024-03-05T12:50:36Z)
• SoFA: Shielded On-the-fly Alignment via Priority Rule Following [90.32819418613407]
  This paper introduces a novel alignment paradigm, priority rule following, which defines rules as the primary control mechanism in each dialog. We present PriorityDistill, a semi-automated approach for distilling priority following signals from simulations to ensure robust rule integration and adherence.
  arXiv  Detail & Related papers  (2024-02-27T09:52:27Z)
• 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)
• Concise and interpretable multi-label rule sets [13.416159628299779]
  We develop a multi-label classifier that can be represented as a concise set of simple "if-then" rules. Our method is able to find a small set of relevant patterns that lead to accurate multi-label classification.
  arXiv  Detail & Related papers  (2022-10-04T11:23:50Z)
• Towards Target Sequential Rules [52.4562332499155]
  We propose an efficient algorithm, called targeted sequential rule mining (TaSRM) It is shown that the novel algorithm TaSRM and its variants can achieve better experimental performance compared to the existing baseline algorithm.
  arXiv  Detail & Related papers  (2022-06-09T18:59:54Z)
• 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)
• Active Learning from Crowd in Document Screening [76.9545252341746]
  We focus on building a set of machine learning classifiers that evaluate documents, and then screen them efficiently. We propose a multi-label active learning screening specific sampling technique -- objective-aware sampling. We demonstrate that objective-aware sampling significantly outperforms the state of the art active learning sampling strategies.
  arXiv  Detail & Related papers  (2020-11-11T16:17:28Z)
• Rewriting a Deep Generative Model [56.91974064348137]
  We introduce a new problem setting: manipulation of specific rules encoded by a deep generative model. We propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory. We present a user interface to enable users to interactively change the rules of a generative model to achieve desired effects.
  arXiv  Detail & Related papers  (2020-07-30T17:58:16Z)
• Interaction Matching for Long-Tail Multi-Label Classification [57.262792333593644]
  We present an elegant and effective approach for addressing limitations in existing multi-label classification models. By performing soft n-gram interaction matching, we match labels with natural language descriptions.
  arXiv  Detail & Related papers  (2020-05-18T15:27:55Z)
• 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)
• Low-Budget Label Query through Domain Alignment Enforcement [48.06803561387064]
  We tackle a new problem named low-budget label query. We first improve an Unsupervised Domain Adaptation (UDA) method to better align source and target domains. We then propose a simple yet effective selection method based on uniform sampling of the prediction consistency distribution.
  arXiv  Detail & Related papers  (2020-01-01T16:52:44Z)

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.