FeatureBand: A Feature Selection Method by Combining Early Stopping and Genetic Local Search
| Autor: | Bin Cui, Huanran Xue, Yingxia Shao, Jiawei Jiang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
050101 languages & linguistics
Early stopping Computer science business.industry 05 social sciences Training time Feature selection 02 engineering and technology Machine learning computer.software_genre Iterative framework Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Search problem 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Local search (optimization) Artificial intelligence business computer |
| Zdroj: | Web and Big Data ISBN: 9783030260743 APWeb/WAIM (2) |
| DOI: | 10.1007/978-3-030-26075-0_3 |
| Popis: | Feature selection is an important problem in machine learning and data mining. In reality, the wrapper methods are broadly used in feature selection. It treats feature selection as a search problem using a predictor as a black-box. However, most wrapper methods are time-consuming due to the large search space. In this paper, we propose a novel wrapper method, called FeatureBand, for feature selection. We use the early stopping strategy to terminate bad candidate feature subsets and avoid wasting of training time. Further, we use a genetic local search to generate new subsets based on previous ones. These two techniques are combined under an iterative framework in which we gradually allocate more resources for more promising candidate feature subsets. The experimental result shows that FeatureBand achieves a better trade-off between search time and search accuracy. It is 1.45\(\times \) to 17.6\(\times \) faster than the state-of-the-art wrapper-based methods without accuracy loss. |
| Databáze: | OpenAIRE |
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