A fast and light classifier for data streams
Analysis of data streams is becoming a key area of data mining research, as the number of applications demanding such processing increases. Modern information technology allows information to be collected at a far greater rate than ever before. Machine learning offers promise of a solution, but the field mainly focuses on achieving high accuracy when data supply is limited. While this has created sophisticated classification algorithms, many do not cope with increasing data set size. When the data set size gets to a point where it could be considered to represent a continuous supply or data stream then incremental classification algorithms are required. When tackling with non-stationary concepts, ensemble of classifiers has several advantages over single classifier methods. They are easy to scale and parallelize, they can adapt to change quickly by pruning underperforming parts of the ensemble and they therefore usually generate more accurate concept descriptions and efficient results. But the effectiveness of an algorithm cannot simply be assessed by accuracy alone. Consideration needs to be given to the memory available to the algorithm and the speed at which data is processed in terms of both the time taken to predict the class of a new data sample and the time taken to include this sample in an incrementally updated classification model. This paper proposes a fast and light classifier for data stream classification.
fast and light boosting sliding window hoeffding tree stream data mining