Recently, incremental and on-line learning gained more attention

especially in the context of big data and learning from data streams,

conflicting with the traditional assumption of complete data availability. Even though

a variety of different methods are available, it often remains unclear which of them is suitable

for a specific task and how they perform in comparison to each other.

We analyze the key properties of seven incremental methods representing different algorithm classes.

Our extensive evaluation on data sets with different characteristics gives an overview of the performance with respect to accuracy as well as model complexity,

facilitating the choice of the best method for a given application.