Practical applications involving big data, such as weather monitoring, identification of
customer preferences, Internet log analysis, and sensors warnings require challenging data …

An attempt has been adopted to predict the As and NO 3− concentration in groundwater
(GW) in fast-growing coastal Ramsar region in eastern India. This study is focused to …

Recent advances in computational intelligent systems have focused on addressing complex
problems related to the dynamicity of the environments. In increasing number of real world …

Several pruning strategies that can be used to reduce the size and increase the accuracy of
bagging ensembles are analyzed. These heuristics select subsets of complementary …

In the real world concepts are often not stable but change with time. A typical example of this
in the biomedical context is antibiotic resistance, where pathogen sensitivity may change …

Soybean is a pivotal agricultural commodity around the world, primarily because of its high
seed protein and oil concentration. Therefore, farmers, breeders and end-users are highly …

Bagging and boosting are two of the most well-known ensemble learning methods due to
their theoretical performance guarantees and strong experimental results. Since bagging …

Random Forests (RF) are a successful ensemble prediction technique that uses majority
voting or averaging as a combination function. However, it is clear that each tree in a …

Hybrid dynamic systems (HDS) combine both discrete and continuous dynamics. Discretely
controlled continuous systems (DCCS) is an important class of HDS in which the system …

This paper is about long-term travel time prediction in public transportation. However, it can
be useful for a wider area of applications. It follows a heterogeneous ensemble approach …