In matters of great importance that have financial, medical, social, or other implications, we
often seek a second opinion before making a decision, sometimes a third, and sometimes …

Over the last two decades, multiple classifier system (MCS) or classifier ensemble has
shown great potential to improve the accuracy and reliability of remote sensing image …

Internet of Things (IoT) plays an increasingly significant role in our daily activities,
connecting physical objects around us into digital services. In other words, IoT is the driving …

Over the last couple of decades, multiple classifier systems, also called ensemble systems
have enjoyed growing attention within the computational intelligence and machine learning …

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

Ensembles of learning machines constitute one of the main current directions in machine
learning research, and have been applied to a wide range of real problems. Despite of the …

摘要集成学习因其能显著提高一个学习系统的泛化能力而得到了机器学习界的广泛关注,
但随着基学习机数目的增多, 集成学习机的预测速度明显下降, 其所需的存储空间也迅速增加 …

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 …

When more than a single classifier has been trained for the same recognition problem the
question arises how this set of classifiers may be combined into a final decision rule. Several …

One of the most important tasks in pattern, machine learning, and data mining is
classification problem. Introducing a general classifier is a challenge for pattern recognition …