Binary Feature Mask Optimization for Feature Selection

• URL: http://arxiv.org/abs/2401.12644v3
• Date: Sun, 08 Dec 2024 10:49:50 GMT
• Title: Binary Feature Mask Optimization for Feature Selection
• Authors: Mehmet E. Lorasdagi, Mehmet Y. Turali, Suleyman S. Kozat,
• Abstract summary: We introduce a novel framework that selects features considering the outcomes of the model. We obtain the mask operator using the predictions of the machine learning model. We demonstrate significant performance improvements on the real-life datasets.
• Score: 0.0
• License:
• Abstract: We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same machine learning model during feature selection, unlike other feature selection methods where we need to train the machine learning model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the machine learning model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, to our knowledge, no study has introduced a training-free framework for a generic machine learning model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and Multi-Layer Perceptron as our machine learning models. The high performance of our General Binary Mask Optimization algorithm stems from its feature masking approach to select features and its flexibility in the number of selected features. The algorithm selects features based on the validation performance of the machine learning model. Hence, the number of selected features is not predetermined and adjusts dynamically to the dataset. Additionally, we openly share the implementation or our code to encourage further research in this area.

Related papers

• Feature Selection for Latent Factor Models [2.07180164747172]
  Feature selection is crucial for pinpointing relevant features in high-dimensional datasets. Traditional feature selection methods for classification use data from all classes to select features for each class. This paper explores feature selection methods that select features for each class separately, using class models based on low-rank generative methods.
  arXiv  Detail & Related papers  (2024-12-13T13:20:10Z)
• Shap-Select: Lightweight Feature Selection Using SHAP Values and Regression [0.0]
  This paper presents a novel feature selection framework, shap-select. The framework conducts a linear or logistic regression of the target on the Shapley values of the features, on the validation set, and uses the signs and significance levels of the regression coefficients to implement an efficient for feature selection. We evaluate shap-select on the Kaggle credit card fraud dataset, demonstrating its effectiveness compared to established methods.
  arXiv  Detail & Related papers  (2024-10-09T12:14:06Z)
• Feature Selection as Deep Sequential Generative Learning [50.00973409680637]
  We develop a deep variational transformer model over a joint of sequential reconstruction, variational, and performance evaluator losses. Our model can distill feature selection knowledge and learn a continuous embedding space to map feature selection decision sequences into embedding vectors associated with utility scores.
  arXiv  Detail & Related papers  (2024-03-06T16:31:56Z)
• A Contrast Based Feature Selection Algorithm for High-dimensional Data set in Machine Learning [9.596923373834093]
  We propose a novel filter feature selection method, ContrastFS, which selects discriminative features based on the discrepancies features shown between different classes. We validate effectiveness and efficiency of our approach on several widely studied benchmark datasets, results show that the new method performs favorably with negligible computation.
  arXiv  Detail & Related papers  (2024-01-15T05:32:35Z)
• 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)
• Deep Feature Selection Using a Novel Complementary Feature Mask [5.904240881373805]
  We deal with feature selection by exploiting the features with less importance scores. We propose a feature selection framework based on a novel complementary feature mask. Our method is generic and can be easily integrated into existing deep-learning-based feature selection approaches.
  arXiv  Detail & Related papers  (2022-09-25T18:03:30Z)
• Compositional Fine-Grained Low-Shot Learning [58.53111180904687]
  We develop a novel compositional generative model for zero- and few-shot learning to recognize fine-grained classes with a few or no training samples. We propose a feature composition framework that learns to extract attribute features from training samples and combines them to construct fine-grained features for rare and unseen classes.
  arXiv  Detail & Related papers  (2021-05-21T16:18:24Z)
• Auto-weighted Multi-view Feature Selection with Graph Optimization [90.26124046530319]
  We propose a novel unsupervised multi-view feature selection model based on graph learning. The contributions are threefold: (1) during the feature selection procedure, the consensus similarity graph shared by different views is learned. Experiments on various datasets demonstrate the superiority of the proposed method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-11T03:25:25Z)
• Feature Selection Using Reinforcement Learning [0.0]
  The space of variables or features that can be used to characterize a particular predictor of interest continues to grow exponentially. Identifying the most characterizing features that minimizes the variance without jeopardizing the bias of our models is critical to successfully training a machine learning model.
  arXiv  Detail & Related papers  (2021-01-23T09:24:37Z)
• Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification [91.67977602992657]
  We propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training.
  arXiv  Detail & Related papers  (2020-03-20T15:44:17Z)
• Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning [100.83444258562263]
  We propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of model selection in this setting. In order to solve the resulting multiple black-box function optimization problem jointly and efficiently, we exploit potential correlations among black-box functions. We are the first to formulate the problem of stepwise model selection (SMS) for sequence prediction, and to design and demonstrate an efficient joint-learning algorithm for this purpose.
  arXiv  Detail & Related papers  (2020-01-12T09:42:19Z)

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.