The crucial role of customers' positive experience and their subsequent word-of-mouth have
been highlighted by both scholars and practitioners for all industry sectors. In response to an …

Multiple classifier fusion may generate more accurate classification than each of the
constituent classifiers. Fusion is often based on fixed combination rules like the product and …

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 …

In unsupervised outlier ensembles, the absence of ground truth makes the combination of
base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles …

Individual classification models are recently challenged by combined pattern recognition
systems, which often show better performance. In such systems the optimal set of classifiers …

Digital technology and social media have brought numerous benefits to human society.
TripAdvisor, which runs on user-generated content, provides a platform for travelers to …

Researchers from various disciplines such as pattern recognition, statistics, and machine
learning have explored the use of ensemble methodology since the late seventies. Thus …

In handwritten pattern recognition, the multiple classifier system has been shown to be
useful for improving recognition rates. One of the most important tasks in optimizing a …

Data mining is often referred to by real-time users and software solutions providers as
knowledge discovery in databases (KDD). Good data mining practice for business …

For any pattern classification task, an increase in data size, number of classes, dimension of
the feature space, and interclass separability affect the performance of any classifier. A …