One of the most important obstacles to deploying predictive models is the fact that humans
do not understand and trust them. Knowing which variables are important in a model's …

Associative classifiers have proven to be very effective in classification problems.
Unfortunately, the algorithms used for learning these classifiers are not able to adequately …

Dynamic programming (DP) and Markov Decision Process (MDP) offer powerful tools for
formulating, modeling, and solving decision making problems under uncertainty. In real …

В работе описаны основные результаты, модели и методы, разработанные в области
ассоциативной классификации, ориентированные на обработку данных большого …

Text classification is one of the methods used for managing, organising and retrieving the
needed data among the huge available text. Several methods have been proposed to …

As one of the most promising classification approaches, association classification (AC)
integrates data classification and association discovery techniques for generating a compact …

As one of the most promising classification schemes, association classification (AC)
effectively integrates classification with association rule discovery, but it requires precisely …

Several Apriori algorithm implementations for mining association rules have been proposed
in the literature using the Hadoop-MapReduce framework and, more recently, Spark …

El microcentro de la ciudad de Bahía Blanca (Argentina) fue muy afectado por la pandemia
y la crisis económica. La circulación está cayendo abruptamente y se ha producido el cierre …

Predicting the number of patients helps managers to allocate resources in hospitals
efficiently. In this research, the relationship between the number of patients with the …