Over the last two decades, multiple classifier system (MCS) or classifier ensemble has
shown great potential to improve the accuracy and reliability of remote sensing image …

Over the last couple of decades, multiple classifier systems, also called ensemble systems
have enjoyed growing attention within the computational intelligence and machine learning …

Many search strategies have been exploited for the task of feature selection (FS), in an effort
to identify more compact and better quality subsets. Such work typically involves the use of …

In supervised learning‐based classification, ensembles have been successfully employed to
different application domains. In the literature, many researchers have proposed different …

Ensemble learning is a method to improve the performance of classification and prediction
algorithms. Many studies have demonstrated that ensemble learning can decrease the …

To build a successful customer churn prediction model, a classification algorithm should be
chosen that fulfills two requirements: strong classification performance and a high level of …

OBJECTIVE: Acute leukemia is a malignant disease that affects a large proportion of the
world population. Different types and subtypes of acute leukemia require different …

Biometrics that use physiological traits such as face, iris, fingerprints, ear, and finger knuckle
(FK) for authentication face the problems of noisy sensors data, non-universality, and …

Error-correcting output coding (ECOC) is a strategy to create classifier ensembles which
reduces a multi-class problem into some binary sub-problems. A key issue in designing any …

Online Reputation Management is a novel and active area in Computational Linguistics.
Closely related to opinion mining and sentiment analysis, it incorporates new features to …