A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

We consider the problem of n-class classification (n\geq2), where the classifier can choose
to abstain from making predictions at a given cost, say, a factor α of the cost of …

Nowadays, a lot of new classification and clustering techniques have been proposed for
microarray data analysis. However, the multiclass microarray data classification is still …

Classifiers with rejection are essential in real-world applications where misclassifications
and their effects are critical. However, if no problem specific cost function is defined, there …

Abstract Error-Correcting Output Codes (ECOC) algorithms enable multiclass classification
by reassigning multiple classes to the positive/negative group with the class reassignment …

Abstract Error-Correcting Output Codes (ECOC) is widely deployed to tackle the multiclass
classification problem by reducing the original multi-class problem to several binary sub …

Partial label learning (PLL) is a type of weakly supervised learning. This paper proposes a
new heuristic algorithm for Partial Label learning through the combination of Feature space …

This paper proposes a novel genetic algorithm (GA) for the error correction output coding
(ECOC) framework. Different from other GA algorithms, a new individual structure is …

In the pattern recognition field, error-correcting output codes (ECOC) are a powerful tool to
fuse any number of binary classifiers to model multiclass problems, and the research of …

Up to now, different genetic algorithm (GA) based error correcting output codes (ECOC)
algorithms have been proposed by setting accuracy as the optimization objective. However …