Artículos regulares

 

Tratamiento del desbalance en problemas con múltiples clases con ECOC

 

Handling the Multi-Class Imbalance Problem using ECOC

 

Rosa María Valdovinos Rosas¹, Rosalinda Abad Sánchez, Roberto Alejo Eleuterio², Edgar Herrera Arteaga ^1,3, Adrián Trueba Espinosa⁴

 

¹ Universidad Autónoma del Estado de México, Facultad de Ingeniería, Ciudad Universitaria, Toluca, México. li_rmvr@hotmail.com

² Tecnológico de Estudios Superiores de Jocotitlán, México. ralejoll@hotmail.com

³ Instituto Nacional de Investigación Nuclear ININ, La Marquesa, Ocoyoacac, México. edgar.herrera@inin.gob.mx

⁴ Centro Universitario UAEM Texoco, México.

 

Article received on 05/11/2012
Accepted on 21/06/2013

 

Resumen

El problema del desbalance de clases puede producir un deterioro importante en la efectividad del clasificador, en particular con los patrones de las clases menos representadas. El desbalance en el conjunto de entrenamiento (CE) significa que una clase es representada por una gran cantidad de patrones mientras que otra es representada por muy pocos. Los estudios existentes se encuentran orientados principalmente a tratar problemas de dos clases, no obstante, un importante número de problemas reales se encuentran representados por múltiples clases, donde resulta más difícil su discriminación para el clasificador. El éxito de la Mezcla de Expertos (ME) se basa en el criterio de "divide y vencerás". En su funcionamiento general, el problema es dividido en fragmentos más pequeños que serán estudiados por separado. De este modo, el modelo general es poco influenciado por las dificultades individuales de sus componentes. La idea principal del estudio aquí mostrado, es construir una Mezcla de expertos cuyos miembros serán entrenados en una parte del problema general y de este modo, mejorar el rendimiento del clasificador en el contexto de múltiples clases. Para este fin, se hace uso de los métodos conocidos como Error-correcting output codes (ECOC), que permiten realizar una codificación en parejas de clases el problema de estudio. Resultados experimentales sobre conjuntos de datos reales, muestran la viabilidad de la estrategia aquí propuesta.

Palabras clave: Desbalance de clases, mezcla de expertos, fusión, múltiples clases, error correcting output codes (ECOC).

 

Abstract

Imbalanced training sample means that one class is represented by a large number of examples while the other is represented by only a few. This problem may produce an important deterioration of the classifier performance, in particular with patterns belonging to the less represented classes. The majority of the studies in this area are oriented, mainly, to resolve problems with two classes. However, many real problems are represented by multiple classes, where it is more difficult to discriminate between them. The success of the Mixture of Experts (ME) strategy is based on the criterion of "divide and win". The general process divides the global problem into smaller fragments which will be studied separately. In this way, the general model has few influences of the individual difficulties (of their members). In this paper we propose a strategy for handling the class imbalance problem for data sets with multiple classes. For that, we integrate a mixture of experts whose members will be trained as a part of the general problem and, in this way, will improve the behavior of the whole system. For dividing the problem we employ the called Error-correcting output codes (ECOC) methods, when the classes are codified in pairs, which are considered for training the mixture of experts. Experiments with real datasets demonstrate the viability of the proposed strategy.

Keywords: Class imbalance, fusion, mixture of experts, error correcting output codes (ECOC).

 

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Agradecimientos

Este trabajo fue realizado gracias al apoyo recibido de los proyectos: 3072/2011 de la UAEM, PROMEP/103.5/12/4783 de las SEP, SDMAIA-010 del TESjo, UR-001 del ININ.

 

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