Towards a Parallel Computationally Efficient Approach to Scaling Up Data Stream Classification

Abstract

2014)Towards a parallel computationally efficient approach to scaling up data stream classification.Abstract Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in realtime. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks. ety (possible unstructured format) of 'Big Data'. Traditional data mining methods for classification of static data take several passes through the training data in order to generate the classification model, which is then applied on previously unseen data instances. Streaming models differ from this learning procedure of Train and Test to a system that continuously needs to be evaluated and updated. As the data is often either too fast to process in depth, or too vast to store, data stream classifiers must be naturally incremental, adaptive and responsive to single exposures to data instances. The continuous task of re-learning and adaptation aims to tackle the problem of concept drift [12] (changes of the patterns encoded in the streams over time). An ideal data stream classifier should incorporate certain features [18]: the classifier must limit its size (memory footprint) as streams are theoretically infinitely long; the time taken to process each instance is short and constant so as not to create a bottleneck; each data instance is only seen once by the classifier; the classification model created incrementally should be equivalent to a 'batch' learner given the same training data; and the classifier must be able to handle concept drift. Data streams come in all forms as technologies merge and become more interconnected. Classic applications are: sensor networks; Internet traffic management and web log analysis [13]; TCP/IP packet monitoring [8]; and intrusion detection [15]. However, capturing, storing and processing these data streams is not feasible, as the data stream is potentially infinite. Systems...

Fast Adaptive Real-Time Classification for Data Streams with Concept Drift

“…A version of the stream with probability P(0) chance of reversing the direction of the concept drift was also created. MC-NN was compared against Hoeffding Trees [3], incremental Naïve Bayes and real-time KNN classifier [12]. Each instance was tested upon the classifier to log the classifier's performance before being used for training: this is also know as prequential testing.…”

“…In the authors' previous feasibility study [12], a parallel real-time classifier was implemented based upon KNN. In KNN a data instance is assigned the class that is most common amongst its K nearest neighbours.…”

“…Realtime KNN retrains on recent instances whilst older instances are deleted. However, real-time KNN is computationally slow with faster data streams [12].…”