An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization

• URL: http://arxiv.org/abs/2504.06207v1
• Date: Tue, 08 Apr 2025 16:51:22 GMT
• Title: An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization
• Authors: Moncef Garouani,
• Abstract summary: We provide an overview of the state of the art in this continuously evolving field.<n>AutoML makes machine learning techniques accessible to domain scientists who are interested in applying advanced analytics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Considerable progress has been made in the recent literature studies to tackle the Algorithms Selection and Parametrization (ASP) problem, which is diversified in multiple meta-learning setups. Yet there is a lack of surveys and comparative evaluations that critically analyze, summarize and assess the performance of existing methods. In this paper, we provide an overview of the state of the art in this continuously evolving field. The survey sheds light on the motivational reasons for pursuing classifiers selection through meta-learning. In this regard, Automated Machine Learning (AutoML) is usually treated as an ASP problem under the umbrella of the democratization of machine learning. Accordingly, AutoML makes machine learning techniques accessible to domain scientists who are interested in applying advanced analytics but lack the required expertise. It can ease the task of manually selecting ML algorithms and tuning related hyperparameters. We comprehensively discuss the different phases of classifiers selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we propose a benchmark knowledge base of 4 millions previously learned models and present extensive comparative evaluations of the prominent methods for classifiers selection based on 08 classification algorithms and 400 benchmark datasets. The comparative study quantitatively assesses the performance of algorithms selection methods along while emphasizing the strengths and limitations of existing studies.

Related papers

• Evaluation of Artificial Intelligence Methods for Lead Time Prediction in Non-Cycled Areas of Automotive Production [1.3499500088995464]
  The present study examines the effectiveness of applying Artificial Intelligence methods in an automotive production environment.<n>Data structures are analyzed to identify contextual features and then preprocessed using one-hot encoding.<n>The research demonstrates that AI methods can be effectively applied to highly variable production data, adding business value.
  arXiv  Detail & Related papers  (2025-01-13T13:28:03Z)
• MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs [97.94579295913606]
  Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia.<n>In the development process, evaluation is critical since it provides intuitive feedback and guidance on improving models.<n>This work aims to offer researchers an easy grasp of how to effectively evaluate MLLMs according to different needs and to inspire better evaluation methods.
  arXiv  Detail & Related papers  (2024-11-22T18:59:54Z)
• Enhancing literature review with LLM and NLP methods. Algorithmic trading case [0.0]
  This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we identified 14,342 relevant articles published between 1956 and Q1 2020.
  arXiv  Detail & Related papers  (2024-10-23T13:37:27Z)
• System for systematic literature review using multiple AI agents: Concept and an empirical evaluation [5.194208843843004]
  We introduce a novel multi-AI agent model designed to fully automate the process of conducting Systematic Literature Reviews. The model operates through a user-friendly interface where researchers input their topic. It generates a search string used to retrieve relevant academic papers. The model then autonomously summarizes the abstracts of these papers.
  arXiv  Detail & Related papers  (2024-03-13T10:27:52Z)
• A Survey of Contextual Optimization Methods for Decision Making under Uncertainty [47.73071218563257]
  This review article identifies three main frameworks for learning policies from data and discusses their strengths and limitations. We present the existing models and methods under a uniform notation and terminology and classify them according to the three main frameworks.
  arXiv  Detail & Related papers  (2023-06-17T15:21:02Z)
• Explainable Model-specific Algorithm Selection for Multi-Label Classification [6.442438468509492]
  Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance can simultaneously belong to multiple classes. Several MLC algorithms have been proposed in the literature, resulting in a meta-optimization problem. We investigate in this work the quality of an automated approach that uses characteristics of the datasets.
  arXiv  Detail & Related papers  (2022-11-21T07:42:11Z)
• Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning [57.163525407022966]
  Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to tackle imbalanced learning through generating synthetic samples for the minority class. We propose AutoSMOTE, an automated over-sampling algorithm that can jointly optimize different levels of decisions.
  arXiv  Detail & Related papers  (2022-08-26T04:28:01Z)
• Towards Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective [67.45111837188685]
  Class incremental learning (CIL) algorithms aim to continually learn new object classes from incrementally arriving data. We experimentally analyze neural network models trained by CIL algorithms using various evaluation protocols in representation learning.
  arXiv  Detail & Related papers  (2022-06-16T11:44:11Z)
• A survey on multi-objective hyperparameter optimization algorithms for Machine Learning [62.997667081978825]
  This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms. We distinguish between metaheuristic-based algorithms, metamodel-based algorithms, and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.
  arXiv  Detail & Related papers  (2021-11-23T10:22:30Z)
• Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning [113.05118113697111]
  Few-shot learning aims to adapt knowledge learned from previous tasks to novel tasks with only a limited amount of labeled data. Research literature on few-shot learning exhibits great diversity, while different algorithms often excel at different few-shot learning scenarios. We present Meta Navigator, a framework that attempts to solve the limitation in few-shot learning by seeking a higher-level strategy.
  arXiv  Detail & Related papers  (2021-09-13T07:20:01Z)
• Hybrid Method Based on NARX models and Machine Learning for Pattern Recognition [0.0]
  This work presents a novel technique that integrates the methodologies of machine learning and system identification to solve multiclass problems. The efficiency of the method was tested by running case studies investigated in machine learning, obtaining better absolute results when compared with classical classification algorithms.
  arXiv  Detail & Related papers  (2021-06-08T00:17:36Z)
• A User's Guide to Calibrating Robotics Simulators [54.85241102329546]
  This paper proposes a set of benchmarks and a framework for the study of various algorithms aimed to transfer models and policies learnt in simulation to the real world. We conduct experiments on a wide range of well known simulated environments to characterize and offer insights into the performance of different algorithms. Our analysis can be useful for practitioners working in this area and can help make informed choices about the behavior and main properties of sim-to-real algorithms.
  arXiv  Detail & Related papers  (2020-11-17T22:24:26Z)

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.