Incremental learning has been widely addressed in the machine learning literature to cope
with learning tasks where the learning environment is ever changing or training samples …

In this article, we introduce selective bagging based incremental learning, an algorithm for
incremental learning using selective ensemble. Selective bagging gains new information …

Over the recent years concept-evolution has received a lot of attention because of its
importance in the context of mining data streams. Mining data stream has become an …

Abstract Knowledge discovery data (KDD) is a research theme evolving to exploit a large
data set collected every day from various fields of computing applications. The underlying …

In recent years, some methods have been presented for dealing with concept maps
construction to provide adaptive learning guidance to learners. In this paper, we present a …

Traditional data mining methods are no longer applicable with the rapid growth of data scale
and complexity, while incremental learning can update existing model incrementally …

One of the problems of individualized classes which adapt contents and methods of
teaching to students of different cognitive capabilities is early and widely available detection …

This paper presents a methodology for using heuristic search methods to optimize
scheduling problem. Specifically, an estimation of distribution algorithm (EDA)-population …

Anomaly Detection in health care by monitoring the vital health parameters of patients is a
challenging problem in machine learning. The existing algorithms do not process the data …