TITRE: META-DES: A dynamic ensemble selection framework using meta-learning
QUAND : Mardi 23 Juin 2015, 10h30
OU : Nano INNOV; bâtiment N2 salle salle 24/25
QUI : Robert SABOURIN, professeur université de Montreal

RESUME :

Dynamic ensemble selection systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of a base classifier, such as, its accuracy in local regions of the feature space around the query instance. However, using only one criterion about the behavior of a base classifier is not sufficient to accurately estimate its level of competence. In this paper, we present a novel dynamic ensemble selection framework using metalearning. We propose five distinct sets of meta-features, each one corresponding to a different criterion to measure the level of competence of a classifier for the classification of input samples. The metafeatures are extracted from the training data and used to train a meta-classifier to predict whether or not a base classifier is competent enough to classify an input instance. During the generalization phase, the meta-features are extracted from the query instance and passed down as input to the metaclassifier. The meta-classifier estimates, whether a base classifier is competent enough to be added to the ensemble. Experiments are conducted over several small sample size classification problems, i.e., problems with a high degree of uncertainty due to the lack of training data. Experimental results show the proposed meta-learning framework greatly improves classification accuracy when compared against current state-of-the-art dynamic ensemble selection techniques.

Reference:

Cruz, Rafael MO, Robert Sabourin, George DC Cavalcanti, and Tsang Ing Ren. « META-DES: A dynamic ensemble selection framework using meta-learning », Pattern Recognition 48, no. 5 (2015): 1925-1935.

NOTE : PS : Ce séminaire sera présenté en français.

Biographie:

Dr. R. Sabourin joined in 1977 the physics department of the Montreal University where he was responsible for the design, experimentation and development of scientific instrumentation for the Mont Mégantic Astronomical Observatory. His main contribution was the design and the implementation of a microprocessor-based fine tracking system combined with a low-light level CCD detector. In 1983, he joined the staff of the École de Technologie Supérieure, Université du Québec, in Montréal where he co-founded the Dept. of Automated Manufacturing Engineering where he is currently Full Professor and teaches Pattern Recognition, Evolutionary Algorithms, Neural Networks and Fuzzy Systems. In 1992, he joined also the Computer Science Department of the Pontifícia Universidade Católica do Paraná (Curitiba, Brazil) where he was, co-responsible for the implementation in 1995 of a master program and in 1998 a PhD program in applied computer science. Since 1996, he is a senior member of the Centre for Pattern Recognition and Machine Intelligence (CENPARMI, Concordia University). Since 2012, he is the Research Chair holder specializing in Adaptive Surveillance Systems in Dynamic Environments.

Dr Sabourin is the author (and co-author) of more than 350 scientific publications including journals and conference proceedings. He was co-chair of the program committee of CIFED’98 (Conférence Internationale Francophone sur l’Écrit et le Document, Québec, Canada) and IWFHR’04 (9th International Workshop on Frontiers in Handwriting Recognition, Tokyo, Japan). He was nominated as Conference co-chair of ICDAR’07 (9th International Conference on Document Analysis and Recognition) that has been held in Curitiba, Brazil in 2007.

His research interests are in the areas of adaptive biometric systems, adaptive surveillance systems in dynamic environments, intelligent watermarking systems, evolutionary computation and bio-cryptography.