Semantic-Guided RL for Interpretable Feature Engineering

• URL: http://arxiv.org/abs/2410.02519v1
• Date: Thu, 3 Oct 2024 14:28:05 GMT
• Title: Semantic-Guided RL for Interpretable Feature Engineering
• Authors: Mohamed Bouadi, Arta Alavi, Salima Benbernou, Mourad Ouziri,
• Abstract summary: We introduce SMART, a hybrid approach that uses semantic technologies to guide the generation of interpretable features. Our experiments on public datasets demonstrate that SMART significantly improves prediction accuracy while ensuring a high level of interpretability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The quality of Machine Learning (ML) models strongly depends on the input data, as such generating high-quality features is often required to improve the predictive accuracy. This process is referred to as Feature Engineering (FE). However, since manual feature engineering is time-consuming and requires case-by-case domain knowledge, Automated Feature Engineering (AutoFE) is crucial. A major challenge that remains is to generate interpretable features. To tackle this problem, we introduce SMART, a hybrid approach that uses semantic technologies to guide the generation of interpretable features through a two-step process: Exploitation and Exploration. The former uses Description Logics (DL) to reason on the semantics embedded in Knowledge Graphs (KG) to infer domain-specific features, while the latter exploits the knowledge graph to conduct a guided exploration of the search space through Deep Reinforcement Learning (DRL). Our experiments on public datasets demonstrate that SMART significantly improves prediction accuracy while ensuring a high level of interpretability.

Related papers

• Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution [38.53065398127086]
  This study investigates the potential of feature attribution methods to filter out uninformative features in input data for regression problems. We introduce a feature selection pipeline that combines Integrated Gradients with k-means clustering to select an optimal set of variables from the initial data space. To validate the effectiveness of this approach, we apply it to a real-world industrial problem - blade vibration analysis in the development process of turbo machinery.
  arXiv  Detail & Related papers  (2024-09-25T09:50:51Z)
• Localized Gaussians as Self-Attention Weights for Point Clouds Correspondence [92.07601770031236]
  We investigate semantically meaningful patterns in the attention heads of an encoder-only Transformer architecture. We find that fixing the attention weights not only accelerates the training process but also enhances the stability of the optimization.
  arXiv  Detail & Related papers  (2024-09-20T07:41:47Z)
• Leveraging Knowlegde Graphs for Interpretable Feature Generation [0.0]
  KRAFT is an AutoFE framework that leverages a knowledge graph to guide the generation of interpretable features. Our hybrid AI approach combines a neural generator to transform raw features through a series of transformations and a knowledge-based reasoner to evaluate features interpretability. The generator is trained through Deep Reinforcement Learning (DRL) to maximize the prediction accuracy and the interpretability of the generated features.
  arXiv  Detail & Related papers  (2024-06-01T19:51:29Z)
• Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization [3.6393183544320236]
  Speech recognition has become an important challenge when using deep learning (DL) It requires large-scale training datasets and high computational and storage resources. Deep transfer learning (DTL) has been introduced to overcome these issues.
  arXiv  Detail & Related papers  (2023-04-27T21:08:05Z)
• On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine Learning [11.670797168818773]
  Key research question is whether it is possible and practical to preemptively avoid costly evaluations of poorly performing ML pipelines. Numerous experiments with the AutoWeka4MCPS package suggest that opportunistic/systematic meta-knowledge can improve ML outcomes. We observe strong sensitivity to the challenge' of a dataset, i.e. whether specificity in choosing a predictor leads to significantly better performance.
  arXiv  Detail & Related papers  (2022-08-08T19:22:24Z)
• KAT: A Knowledge Augmented Transformer for Vision-and-Language [56.716531169609915]
  We propose a novel model - Knowledge Augmented Transformer (KAT) - which achieves a strong state-of-the-art result on the open-domain multimodal task of OK-VQA. Our approach integrates implicit and explicit knowledge in an end to end encoder-decoder architecture, while still jointly reasoning over both knowledge sources during answer generation. An additional benefit of explicit knowledge integration is seen in improved interpretability of model predictions in our analysis.
  arXiv  Detail & Related papers  (2021-12-16T04:37:10Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)
• Pre-Trained Models: Past, Present and Future [126.21572378910746]
  Large-scale pre-trained models (PTMs) have recently achieved great success and become a milestone in the field of artificial intelligence (AI) By storing knowledge into huge parameters and fine-tuning on specific tasks, the rich knowledge implicitly encoded in huge parameters can benefit a variety of downstream tasks. It is now the consensus of the AI community to adopt PTMs as backbone for downstream tasks rather than learning models from scratch.
  arXiv  Detail & Related papers  (2021-06-14T02:40:32Z)
• Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing [57.52865154829273]
  Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience remote sensing (RS) In the past decade efforts have been made to process analyze these hyperspectral (HS) products mainly by means of seasoned experts. For this reason, it is urgent to develop more intelligent and automatic approaches for various HS RS applications.
  arXiv  Detail & Related papers  (2021-03-02T03:32:10Z)
• Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions [4.932130498861987]
  We show that a surprisingly small number of features -- often less than four -- can suffice to achieve a 98% accuracy. We show that the classification accuracy transfers to settings in which several instances are involved in training and testing.
  arXiv  Detail & Related papers  (2021-02-01T10:04:28Z)

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.