Co-Multistage of Multiple Classifiers for Imbalanced Multiclass Learning

Autor:	Marujo, Luis, Gershman, Anatole, Carbonell, Jaime, de Matos, David Martins, Neto, João P.
Rok vydání:	2013
Předmět:	Computer Science - Learning Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	In this work, we propose two stochastic architectural models (CMC and CMC-M) with two layers of classifiers applicable to datasets with one and multiple skewed classes. This distinction becomes important when the datasets have a large number of classes. Therefore, we present a novel solution to imbalanced multiclass learning with several skewed majority classes, which improves minority classes identification. This fact is particularly important for text classification tasks, such as event detection. Our models combined with pre-processing sampling techniques improved the classification results on six well-known datasets. Finally, we have also introduced a new metric SG-Mean to overcome the multiplication by zero limitation of G-Mean. Comment: Preliminary version of the paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1312.6597 Zobrazit plný text záznamu View this record from Arxiv