While the areas of applications in data mining are growing substantially, it has become
extremely necessary for incremental learning methods to move a step ahead. The …

In most challenging applications learning algorithms act in dynamic environments where the
data is collected over time. A desirable property of these algorithms is the ability of …

A fundamental problem when performing incremental learning is that the best set of a
classification system's parameters can change with the evolution of the data. Consequently …

One of the research topics in machine learning is incremental machine learning. The ever-
increasing data size and variety in response to the limited memory and processing power …

Multiple classifier systems tend to suffer from outvoting when new concept classes need to
be learned incrementally. Out-voting is primarily due to existing classifiers being unable to …

The paper presents a concept drift detection method for unsupervised learning which takes
into consideration the prior knowledge to select the most appropriate classification model …

Classification of the unknown dataset can be obtained by several methods. Ensemble
classifier methods are proved to be the better for classification. Learn++, An incremental …

In this study, an incremental updating technique is applied to associative classification for
constructing classification system when a new training dataset is appended to an old …

We usually suffer from difficulties in treating or managing Big Data generated from various
digital media and/or sensors using traditional mining techniques. Additionally, there are …