Automatic learning algorithm selection for classification via convolutional neural networks

• URL: http://arxiv.org/abs/2305.09101v1
• Date: Tue, 16 May 2023 01:57:01 GMT
• Title: Automatic learning algorithm selection for classification via convolutional neural networks
• Authors: Sebastian Maldonado, Carla Vairetti, Ignacio Figueroa
• Abstract summary: The goal of this study is to learn the inherent structure of the data without identifying meta-features. Experiments with simulated datasets show that the proposed approach achieves nearly perfect performance in identifying linear and nonlinear patterns.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As in any other task, the process of building machine learning models can benefit from prior experience. Meta-learning for classifier selection gains knowledge from characteristics of different datasets and/or previous performance of machine learning techniques to make better decisions for the current modeling process. Meta-learning approaches first collect meta-data that describe this prior experience and then use it as input for an algorithm selection model. In this paper, however, we propose an automatic learning scheme in which we train convolutional networks directly with the information of tabular datasets for binary classification. The goal of this study is to learn the inherent structure of the data without identifying meta-features. Experiments with simulated datasets show that the proposed approach achieves nearly perfect performance in identifying linear and nonlinear patterns, outperforming the traditional two-step method based on meta-features. The proposed method is then applied to real-world datasets, making suggestions about the best classifiers that can be considered based on the structure of the data.

Related papers

• Attribute-to-Delete: Machine Unlearning via Datamodel Matching [65.13151619119782]
  Machine unlearning -- efficiently removing a small "forget set" training data on a pre-divertrained machine learning model -- has recently attracted interest. Recent research shows that machine unlearning techniques do not hold up in such a challenging setting.
  arXiv  Detail & Related papers  (2024-10-30T17:20:10Z)
• Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection [56.292071534857946]
  Recent data-privacy laws have sparked interest in machine unlearning. Challenge is to discard information about the forget'' data without altering knowledge about remaining dataset. We adopt a projected-gradient based learning method, named as Projected-Gradient Unlearning (PGU) We provide empirically evidence to demonstrate that our unlearning method can produce models that behave similar to models retrained from scratch across various metrics even when the training dataset is no longer accessible.
  arXiv  Detail & Related papers  (2023-12-07T07:17:24Z)
• Towards Machine Unlearning Benchmarks: Forgetting the Personal Identities in Facial Recognition Systems [4.985768723667418]
  We propose a machine unlearning setting that aims to unlearn specific instance that contains personal privacy (identity) while maintaining the original task of a given model. Specifically, we propose two machine unlearning benchmark datasets, MUFAC and MUCAC, that are greatly useful to evaluate the performance and robustness of a machine unlearning algorithm.
  arXiv  Detail & Related papers  (2023-11-03T21:00:32Z)
• Clustering Indices based Automatic Classification Model Selection [16.096824533334352]
  We propose a novel method for automatic classification model selection from a set of candidate model classes. We compute the dataset clustering indices and directly predict the expected classification performance using the learned regressor. We also propose an end-to-end Automated ML system for data classification based on our model selection method.
  arXiv  Detail & Related papers  (2023-05-23T10:52:37Z)
• Which is the best model for my data? [0.0]
  The proposed meta-learning approach relies on machine learning and involves four major steps. We present a collection of 62 meta-features that address the problem of information cancellation when aggregation measure values involving positive and negative measurements. We show that our meta-learning approach can correctly predict an optimal model for 91% of the synthetic datasets and for 87% of the real-world datasets.
  arXiv  Detail & Related papers  (2022-10-26T13:15:43Z)
• Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches [0.5801044612920815]
  We propose a novel technique for algorithm-selection, applicable to optimisation in which there is implicit sequential information encapsulated in the data. We train two types of recurrent neural networks to predict a packing in online bin-packing, selecting from four well-known domains.
  arXiv  Detail & Related papers  (2022-03-24T23:59:50Z)
• Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection [55.888835686183995]
  We propose a neural network-based meta-learning method for supervised anomaly detection. We experimentally demonstrate that the proposed method achieves better performance than existing anomaly detection and few-shot learning methods.
  arXiv  Detail & Related papers  (2021-03-01T01:43:04Z)
• Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
  We investigate three schemes to improve the model generalization ability for few-shot settings. We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data. We create new state-of-the-art results on both few-shot and training-free settings.
  arXiv  Detail & Related papers  (2020-12-29T23:43:16Z)
• Fast Few-Shot Classification by Few-Iteration Meta-Learning [173.32497326674775]
  We introduce a fast optimization-based meta-learning method for few-shot classification. Our strategy enables important aspects of the base learner objective to be learned during meta-training. We perform a comprehensive experimental analysis, demonstrating the speed and effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-10-01T15:59:31Z)
• Probabilistic Active Meta-Learning [15.432006404678981]
  We introduce task selection based on prior experience into a meta-learning algorithm. We provide empirical evidence that our approach improves data-efficiency when compared to strong baselines on simulated robotic experiments.
  arXiv  Detail & Related papers  (2020-07-17T12:51:42Z)
• Meta-learning framework with applications to zero-shot time-series forecasting [82.61728230984099]
  This work provides positive evidence using a broad meta-learning framework. residual connections act as a meta-learning adaptation mechanism. We show that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining.
  arXiv  Detail & Related papers  (2020-02-07T16:39:43Z)

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.