Online Semi-supervised Learning for Multi-target Regression in Data Streams Using AMRules

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Ricardo Sousa (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: ADVANCES IN INTELLIGENT DATA ANALYSIS XV

Pages: 123-133

15th International Symposium on Intelligent Data Analysis (IDA)

Stockholm Univ, Dept Comp & Syst Sci, Stockholm, SWEDEN, OCT 13-15, 2016

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00K-WFR

DOI: 10.1007/978-3-319-46349-0_11

Abstract (EN): Most data streams systems that use online Multi-target regression yield vast amounts of data which is not targeted. Targeting this data is usually impossible, time consuming and expensive. Semi-supervised algorithms have been proposed to use this untargeted data (input information only) for model improvement. However, most algorithms are adapted to work on batch mode for classification and require huge computational and memory resources. Therefore, this paper proposes an semi-supervised algorithm for online processing systems based on AMRules algorithm that handle both targeted and untargeted data and improves the regression model. The proposed method was evaluated through a comparison between a scenario where the untargeted examples are not used on the training and a scenario where some untargeted examples are used. Evaluation results indicate that the use of the untargeted examples improved the target predictions by improving the model.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11