Brute force concept drift detection

Abstract

We present a brute-force approach to detect concept drift behind time sequence data. This approach, named Select-Starţ searches for start points of concept drift to minimize error. In other words, Select-Start searches for the start points of new concepts from the input sequence. Unlike many related works, Select-Start does not require a pre-specified error threshold to detect drift. This paper compares Select-Start with previous representative methods and clarifies its characteristics. The experimental results show that Select-Start is accurate for concept drift problems where the threshold changes slowly. However, existing methods are better at analyzing concept drift problems where the model behind data changes rapidly.