Distance Functions and Normalization Under Stream Scenarios
This work addresses normalization challenges for practitioners handling data streams, but it is incremental as it compares existing distance functions under new normalization protocols.
The paper tackled the problem of data normalization in classification systems under data stream scenarios, where feature properties are unknown and may change over time, and found that using the original data stream without normalization combined with the Canberra distance yields good results.
Data normalization is an essential task when modeling a classification system. When dealing with data streams, data normalization becomes especially challenging since we may not know in advance the properties of the features, such as their minimum/maximum values, and these properties may change over time. We compare the accuracies generated by eight well-known distance functions in data streams without normalization, normalized considering the statistics of the first batch of data received, and considering the previous batch received. We argue that experimental protocols for streams that consider the full stream as normalized are unrealistic and can lead to biased and poor results. Our results indicate that using the original data stream without applying normalization, and the Canberra distance, can be a good combination when no information about the data stream is known beforehand.