On Feature Selection Using Anisotropic General Regression Neural Network

• URL: http://arxiv.org/abs/2010.05744v1
• Date: Mon, 12 Oct 2020 14:35:40 GMT
• Title: On Feature Selection Using Anisotropic General Regression Neural Network
• Authors: Federico Amato, Fabian Guignard, Philippe Jacquet and Mikhail Kanevski
• Abstract summary: The presence of irrelevant features in the input dataset tends to reduce the interpretability and predictive quality of machine learning models. Here we show how the General Regression Neural Network used with an anisotropic Gaussian Kernel can be used to perform feature selection.
• Score: 3.880707330499936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The presence of irrelevant features in the input dataset tends to reduce the interpretability and predictive quality of machine learning models. Therefore, the development of feature selection methods to recognize irrelevant features is a crucial topic in machine learning. Here we show how the General Regression Neural Network used with an anisotropic Gaussian Kernel can be used to perform feature selection. A number of numerical experiments are conducted using simulated data to study the robustness of the proposed methodology and its sensitivity to sample size. Finally, a comparison with four other feature selection methods is performed on several real world datasets.

Related papers

• Unveiling the Power of Sparse Neural Networks for Feature Selection [60.50319755984697]
  Sparse Neural Networks (SNNs) have emerged as powerful tools for efficient feature selection. We show that SNNs trained with dynamic sparse training (DST) algorithms can achieve, on average, more than $50%$ memory and $55%$ FLOPs reduction. Our findings show that feature selection with SNNs trained with DST algorithms can achieve, on average, more than $50%$ memory and $55%$ FLOPs reduction.
  arXiv  Detail & Related papers  (2024-08-08T16:48:33Z)
• IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight [4.010646933005848]
  IGANN Sparse is a novel machine learning model from the family of generalized additive models. It promotes sparsity through a non-linear feature selection process during training. This ensures interpretability through improved model sparsity without sacrificing predictive performance.
  arXiv  Detail & Related papers  (2024-03-17T22:44:36Z)
• A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning [131.2910403490434]
  Data scientists typically collect as many features as possible into their datasets, and even engineer new features from existing ones. Existing benchmarks for tabular feature selection consider classical downstream models, toy synthetic datasets, or do not evaluate feature selectors on the basis of downstream performance. We construct a challenging feature selection benchmark evaluated on downstream neural networks including transformers. We also propose an input-gradient-based analogue of Lasso for neural networks that outperforms classical feature selection methods on challenging problems.
  arXiv  Detail & Related papers  (2023-11-10T05:26:10Z)
• Sparse-Input Neural Network using Group Concave Regularization [10.103025766129006]
  Simultaneous feature selection and non-linear function estimation are challenging in neural networks. We propose a framework of sparse-input neural networks using group concave regularization for feature selection in both low-dimensional and high-dimensional settings.
  arXiv  Detail & Related papers  (2023-07-01T13:47:09Z)
• Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks [17.12834153477201]
  We propose a novel resource-efficient supervised feature selection method using sparse neural networks. By gradually pruning the uninformative features from the input layer of a sparse neural network trained from scratch, NeuroFS derives an informative subset of features efficiently. NeuroFS achieves the highest ranking-based score among the considered state-of-the-art supervised feature selection models.
  arXiv  Detail & Related papers  (2023-03-10T17:09:55Z)
• Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
  We construct a dataset of neural network checkpoints and train a generative model on the parameters. We find that our approach successfully generates parameters for a wide range of loss prompts. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
  arXiv  Detail & Related papers  (2022-09-26T17:59:58Z)
• Inducing Gaussian Process Networks [80.40892394020797]
  We propose inducing Gaussian process networks (IGN), a simple framework for simultaneously learning the feature space as well as the inducing points. The inducing points, in particular, are learned directly in the feature space, enabling a seamless representation of complex structured domains. We report on experimental results for real-world data sets showing that IGNs provide significant advances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-21T05:27:09Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach [80.8446673089281]
  We propose a new learning paradigm with graph representation and learning. Our framework contains two modules: 1) a backbone network (e.g., feedforward neural nets) as a lower model takes features as input and outputs predicted labels; 2) a graph neural network as an upper model learns to extrapolate embeddings for new features via message passing over a feature-data graph built from observed data.
  arXiv  Detail & Related papers  (2021-10-09T09:02:45Z)
• Feature Selection Based on Sparse Neural Network Layer with Normalizing Constraints [0.0]
  We propose new neural-network based feature selection approach that introduces two constrains, the satisfying of which leads to sparse FS layer. The results confirm that proposed Feature Selection Based on Sparse Neural Network Layer with Normalizing Constraints (SNEL-FS) is able to select the important features and yields superior performance compared to other conventional FS methods.
  arXiv  Detail & Related papers  (2020-12-11T14:14:33Z)

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.