Confidence factor and feature selection for semi-supervised multi-label classification methods
Fator de confid?ncia em sele??o de caracter?sticas para m?todos de classifica??o semi-supervisionado multi-r?tulo
| dc.creator | Rodrigues, Fillipe | |
| dc.creator | Santos, Araken | |
| dc.creator | Canuto, Anne | |
| dc.date | 2017-06-21T19:40:33Z | |
| dc.date | 2017-06-22 | |
| dc.date | 2017-06-21T19:40:33Z | |
| dc.date | 2014-07-11 | |
| dc.date.accessioned | 2023-09-27T14:18:57Z | |
| dc.date.available | 2023-09-27T14:18:57Z | |
| dc.identifier | http://memoria.ifrn.edu.br/handle/1044/1182 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8920429 | |
| dc.description | In this paper, we investigate two important problems in multi-label classification algorithms, which are: the number of labeled instances and the high dimensionality of the labeled instances. In the literature, we can find several papers about multi-label classification problems, where an instance can be associated with more than one label simultaneously. One of the main issues with multi-label classification methods is that many of these require a high number of instances to be able to generalize in an efficient way. In order to solve this problem, we used semi-supervised learning, which combines labeled and unlabeled instances during the training process. In this sense, the semi-supervised learning may become an essential tool to define, efficiently, the process of automatic assignment of labels. Therefore, this paper presents four semi-supervised methods for the multilabel classification, focusing on the use of a confidence parameter in the process of automatic assignment of labels. In order to validate the feasibility of these methods, an empirical analysis will be conducted using high-dimensional datasets, aiming to evaluate the performance of such methods in different situations. In this case, we will apply a feature selection algorithm in order to reduce, in an efficient way, the number of features to be used by the classification methods. | |
| dc.language | eng | |
| dc.publisher | Instituto Federal de Educa??o, Ci?ncia e Tecnologia do Rio Grande do Norte | |
| dc.publisher | Brasil | |
| dc.publisher | Parnamirim | |
| dc.publisher | IFRN | |
| dc.relation | IJCNN - International Joint Conference on Neural Networks | |
| dc.rights | Acesso Restrito | |
| dc.subject | Multi-label | |
| dc.subject | Semi-supervised | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Ci?ncia da Computa??o | |
| dc.subject | Intelig?ncia Artificial | |
| dc.title | Confidence factor and feature selection for semi-supervised multi-label classification methods | |
| dc.title | Fator de confid?ncia em sele??o de caracter?sticas para m?todos de classifica??o semi-supervisionado multi-r?tulo | |
| dc.type | Artigo de Peri?dico |