Solving the challenges of concept drift in data stream classification.

Abstract

The rise of network connected devices and applications leads to a significant increase in the volume of data that are continuously generated overtime time, called data streams. In real world applications, storing the entirety of a data stream for analyzing later is often not practical, due to the data stream’s potentially infinite volume. Data stream mining techniques and frameworks are therefore created to analyze streaming data as they arrive. However, compared to traditional data mining techniques, challenges unique to data stream mining also emerge, due to the high arrival rate of data streams and their dynamic nature. In this dissertation, an array of techniques and frameworks are presented to improve the solutions on some of the challenges. First, this dissertation acknowledges that a “no free lunch” theorem exists for data stream mining, where no silver bullet solution can solve all problems of data stream mining. The dissertation focuses on detection of changes of data distribution in data stream mining. These changes are called concept drift. Concept drift can be categorized into many types. A detection algorithm often works only on some types of drift, but not all of them. Because of this, the dissertation finds specific techniques to solve specific challenges, instead of looking for a general solution. Then, this dissertation considers improving solutions for the challenges of high arrival rate of data streams. Data stream mining frameworks often need to process vast among of data samples in limited time. Some data mining activities, notably data sample labeling for classification, are too costly or too slow in such large scale. This dissertation presents two techniques that reduce the amount of labeling needed for data stream classification. The first technique presents a grid-based label selection process that apply to highly imbalanced data streams. Such data streams have one class of data samples vastly outnumber another class. Many majority class samples need to be labeled before a minority class sample can be found due to the imbalance. The presented technique divides the data samples into groups, called grids, and actively search for minority class samples that are close by within a grid. Experiment results show the technique can reduce the total number of data samples needed to be labeled. The second technique presents a smart preprocessing technique that reduce the number of times a new learning model needs to be trained due to concept drift. Less model training means less data labels