Advantages of the KnowledgeFlow Interface:

The KnowledgeFlow interface presents an alternative to the Experimenter. It allows a user to construct complex experiments visually and this can make the process easier. Connections are labeled making it easier to follow the experiment and understand what is going on. As well, the interface is designed to prevent the creation of invalid connections as components will only be attached to components can accept connections from.

A major advantage of this interface that we did not look at is that it opens up a whole class of updateable learning schemes. Updateable learning schemes are able to take in data incrementally to continuously improve the model as the data streams in. This ability makes it is possible to take advantage of extremely large or even infinitely large data. This feature is useful in information retrieval applications and in areas such as spam detection in emails. In these cases, the data is often unlimited and constantly changing and an updatable model can always take advantage of new data as it comes in.

Disadvantages of the KnowledgeFlow Interface:

The main disadvantage of the KnowledgeFlow interface is that it is newer and some of Weka’s functionality is not available or not fully implemented. Unlike the Experimenter, this interface has the ability to compare graphically the different models as we did in our example with the 2 ROC curves. Unfortunately there is no way to compare the two models numerically to determine if there are statistically significant differences between them as this functionality has not yet been implemented in this interface.