Automated Model Selection for Tabular Data

• URL: http://arxiv.org/abs/2401.00961v2
• Date: Wed, 29 May 2024 01:03:16 GMT
• Title: Automated Model Selection for Tabular Data
• Authors: Avinash Amballa, Gayathri Akkinapalli, Manas Madine, Naga Pavana Priya Yarrabolu, Przemyslaw A. Grabowicz,
• Abstract summary: R's mixed effect linear models library allows users to provide interactive feature combinations in the model design. We aim to automate the model selection process for predictions on datasets incorporating feature interactions. The framework includes two distinct approaches for feature selection: a Priority-based Random Grid Search and a Greedy Search method.
• Score: 0.1797555376258229
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Structured data in the form of tabular datasets contain features that are distinct and discrete, with varying individual and relative importances to the target. Combinations of one or more features may be more predictive and meaningful than simple individual feature contributions. R's mixed effect linear models library allows users to provide such interactive feature combinations in the model design. However, given many features and possible interactions to select from, model selection becomes an exponentially difficult task. We aim to automate the model selection process for predictions on tabular datasets incorporating feature interactions while keeping computational costs small. The framework includes two distinct approaches for feature selection: a Priority-based Random Grid Search and a Greedy Search method. The Priority-based approach efficiently explores feature combinations using prior probabilities to guide the search. The Greedy method builds the solution iteratively by adding or removing features based on their impact. Experiments on synthetic demonstrate the ability to effectively capture predictive feature combinations.

Related papers

• Neuro-Symbolic Embedding for Short and Effective Feature Selection via Autoregressive Generation [22.87577374767465]
  We reformulate feature selection through a neuro-symbolic lens and introduce a novel generative framework aimed at identifying short and effective feature subsets. In this framework, we first create a data collector to automatically collect numerous feature selection samples consisting of feature ID tokens, model performance, and the measurement of feature subset redundancy. Building on the collected data, an encoder-decoder-evaluator learning paradigm is developed to preserve the intelligence of feature selection into a continuous embedding space for efficient search.
  arXiv  Detail & Related papers  (2024-04-26T05:01:08Z)
• 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)
• Causal Feature Selection via Transfer Entropy [59.999594949050596]
  Causal discovery aims to identify causal relationships between features with observational data. We introduce a new causal feature selection approach that relies on the forward and backward feature selection procedures. We provide theoretical guarantees on the regression and classification errors for both the exact and the finite-sample cases.
  arXiv  Detail & Related papers  (2023-10-17T08:04:45Z)
• Copula for Instance-wise Feature Selection and Ranking [24.09326839818306]
  We propose to incorporate the Gaussian copula, a powerful mathematical technique for capturing correlations between variables, into the current feature selection framework. Experimental results on both synthetic and real datasets, in terms of performance comparison and interpretability, demonstrate that our method is capable of capturing meaningful correlations.
  arXiv  Detail & Related papers  (2023-08-01T13:45:04Z)
• Composite Feature Selection using Deep Ensembles [130.72015919510605]
  We investigate the problem of discovering groups of predictive features without predefined grouping. We introduce a novel deep learning architecture that uses an ensemble of feature selection models to find predictive groups. We propose a new metric to measure similarity between discovered groups and the ground truth.
  arXiv  Detail & Related papers  (2022-11-01T17:49:40Z)
• Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data [4.551615447454768]
  We propose a model-free and sparsity-based automatic feature selection (SAFS) framework to facilitate automatic discovery of divergent subgroups. We validated SAFS across two publicly available datasets (MIMIC-III and Allstate Claims) and compared it with six existing feature selection methods.
  arXiv  Detail & Related papers  (2022-03-08T20:42:56Z)
• 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)
• Joint Adaptive Graph and Structured Sparsity Regularization for Unsupervised Feature Selection [6.41804410246642]
  We propose a joint adaptive graph and structured sparsity regularization unsupervised feature selection (JASFS) method. A subset of optimal features will be selected in group, and the number of selected features will be determined automatically. Experimental results on eight benchmarks demonstrate the effectiveness and efficiency of the proposed method.
  arXiv  Detail & Related papers  (2020-10-09T08:17:04Z)
• 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.