| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Sao Paulo State Univ UNESP, Inst Geosci & Exact Sci IGCE, Dept Stat Appl Math & Computat DEMAC, BR-13506900 Sao Paulo - Brazil
[2] Univ Sao Paulo, Sch Philosophy Sci & Literature Ribeirao Preto FF, Dept Comp Sci & Math DCM, BR-14040900 Sao Paulo - Brazil
[3] Fed Univ Sao Paulo Unifesp, Inst Sci & Technol ICT, Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | Neurocomputing; v. 160, p. 63-72, JUL 21 2015. |
| Web of Science Citations: | 5 |
| Abstract | |
Semi-supervised learning methods are usually employed in the classification of data sets where only a small subset of the data items is labeled. In these scenarios, label noise is a crucial issue, since the noise may easily spread to a large portion or even the entire data set, leading to major degradation in classification accuracy. Therefore, the development of new techniques to reduce the nasty effects of label noise in semi-supervised learning is a vital issue. Recently, a graph-based semi-supervised learning approach based on particle competition and cooperation was developed. In this model, particles walk in the graphs constructed from the data sets. Competition takes place among particles representing different class labels, while the cooperation occurs among particles with the same label. This paper presents a new particle competition and cooperation algorithm, specifically designed to increase the robustness to the presence of label noise, improving its label noise tolerance. Different from other methods, the proposed one does not require a separate technique to deal with label noise. It performs classification of unlabeled nodes and reclassification of the nodes affected by label noise in a unique process. Computer simulations show the classification accuracy of the proposed method when applied to some artificial and real-world data sets, in which we introduce increasing amounts of label noise. The classification accuracy is compared to those achieved by previous particle competition and cooperation algorithms and other representative graph-based semi-supervised learning methods using the same scenarios. Results show the effectiveness of the proposed method. (C) 2015 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 11/17396-9 - Machine learning using models inspired by nature |
| Grantee: | Fabricio Aparecido Breve |
| Support Opportunities: | Research Grants - Young Investigators Grants |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 11/50151-0 - Dynamical phenomena in complex networks: fundamentals and applications |
| Grantee: | Elbert Einstein Nehrer Macau |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 11/18496-7 - Dynamic semi-supervised and active learning based on complex networks |
| Grantee: | Marcos Gonçalves Quiles |
| Support Opportunities: | Research Grants - Young Investigators Grants |