Pruned Sets for Multi-Label Stream Classification without True Labels

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Costa Junior, JD (Author) Other; Faria, ER (Author) Other; Silva, JA (Author) Other; João Gama (Author) FEP; Cerri, R (Author) Other

Conference proceedings International

Title: Proceedings of the International Joint Conference on Neural Networks

2019 International Joint Conference on Neural Networks, IJCNN 2019

14 July 2019 through 19 July 2019

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 8 Citations

Other information

Authenticus ID: P-00R-589

DOI: 10.1109/ijcnn.2019.8851788

Abstract (EN): In multi-label classification problems an example can be simultaneously classified into more than one class. This is also a challenging task in Data Streams (DS) classification, where unbounded and non-stationary distributed multi-label data contain multiple concepts that drift at different rates and patterns. In addition, the true labels of the examples may never become available and updating classification models in a supervised fashion is unfeasible. In this paper, we propose a Multi-Label Stream Classification (MLSC) method applying a Novelty Detection (ND) procedure task to update the classification model detecting any new patterns in the examples, which differ in some aspects from observed patterns, in an unsupervised fashion without any external feedback. Although ND is suitable for multi-class stream classification, it is still a not well-investigated task for multi-label problems. We improve a initial work proposed in [1] and extended it with a new Pruned Sets (PS) transformation strategy. The experiments showed that our method presents competitive performances over data sets with different concept drifts, and outperform, in some aspects, the baseline methods.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8