Publication Type

Journal Article

Publication Date

6-2017

Abstract

Feature selection (FS) is an important technique in machine learning and data mining, especially for largescale high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable when handling big data in real world. As real data may arrive sequentially and continuously, batch learning has to retrain the model for the new coming data, which is very computationally intensive. Online feature selection (OFS) is a promising new paradigm that is more efficient and scalable than batch learning algorithms. However, existing online algorithms usually fall short in their inferior efficacy. In this article, we present a novel second-order OFS algorithm that is simple yet effective, very fast and extremely scalable to deal with large-scale ultra-high dimensional sparse data streams. The basic idea is to exploit the second-order information to choose the subset of important features with high confidence weights. Unlike existing OFS methods that often suffer from extra high computational cost, we devise a novel algorithm with a MaxHeap-based approach, which is not only more effective than the existing firstorder algorithms, but also significantly more efficient and scalable. Our extensive experiments validated that the proposed technique achieves highly competitive accuracy as compared with state-of-The-Art batch FS methods, meanwhile it consumes significantly less computational cost that is orders of magnitude lower. Impressively, on a billion-scale synthetic dataset (1-billion dimensions, 1-billion non-zero features, and 1- million samples), the proposed algorithm takes less than 3 minutes to run on a single PC.

Keywords

Feature selection, Second-order online learning, Sparsity, Ultra-high dimensionality

Discipline

Databases and Information Systems | Numerical Analysis and Computation

Research Areas

Data Management and Analytics

Publication

ACM Transactions on Knowledge Discovery from Data

Volume

11

Issue

4

ISSN

1556-4681

Identifier

10.1145/3070646

Publisher

Association for Computing Machinery (ACM)

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org./10.1145/3070646

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