Comparison Between Co-training and Self-training for Single-target Regression in Data Streams using AMRules

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

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

Conference proceedings International

Title: IOTSTREAMING 2017: IoT Large Scale Learning from Data Streams

2017 Workshop on IoT Large Scale Learning from Data Streams, IOTSTREAMING 2017

18 September 2017 through 22 September 2017

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00N-844

Abstract (EN): A comparison between co-training and self-training method for single-target regression based on multiples learners is performed. Data streaming systems can create a significant amount of unlabeled data which is caused by label assignment impossibility, high cost of labeling or labeling long duration tasks. In supervised learning, this data is wasted. In order to take advantaged from unlabeled data, semi-supervised approaches such as Co-training and Self-training have been created to benefit from input information that is contained in unlabeled data. However, these approaches have been applied to classification and batch training scenarios. Due to these facts, this paper presents a comparison between Co-training and Self-learning methods for single-target regression in data streams. Rules learning is used in this context since this methodology enables to explore the input information. The experimental evaluation consisted of a comparison between the real standard scenario where all unlabeled data is rejected and scenarios where unlabeled data is used to improve the regression model. Results show evidences of better performance in terms of error reduction and in high level of unlabeled examples in the stream. Despite this fact, the improvements are not expressive.

Language: English

Type (Professor's evaluation): Scientific