For many real world problems we must perform classification under widely varying amounts of computational resources. For example, if asked to classify an instance taken from a bursty stream, we may have from milliseconds to minutes to return a class prediction. For such problems an anytime algorithm may be especially useful. In this work we show how we can convert the ubiquitous nearest neighbor classifier into an anytime algorithm that can produce an instant classification, or if given the luxury of additional time, can utilize the extra time to increase classification accuracy. We demonstrate the utility of our approach with a comprehensive set of experiments on data from diverse domains.

Abstract — Classification of items taken from data streams requires algorithms that operate in time sensitive and computationally constrained environments. Often, the available time for classification is not known a priori and may change as a consequence of external circumstances. Many traditional algorithms are unable to provide satisfactory performance while supporting the highly variable response times that exemplify such applications. In such contexts, anytime algorithms, which are amenable to trading time for accuracy, have been found to be exceptionally useful and constitute an area of increasing research activity. Previous techniques for improving anytime classification have generally been concerned with optimizing the probability of correctly classifying individual objects. However, as we shall see, serially optimizing the probability of correctly classifying individual objects K times, generally gives inferior results to batch optimizing the probability of correctly classifying K objects. In this work, we show that this simple observation can be exploited to improve overall classification performance by using an anytime framework to allocate resources among a set of objects buffered from a fast arriving stream. Our ideas are independent of object arrival behavior; and, perhaps unintuitively, even in data streams with constant arrival rates our technique exhibits a marked improvement in performance. The utility of our approach is demonstrated with extensive experimental evaluations conducted on a wide range of diverse datasets. Keywords-anytime algorithms; classification; nearest neighbor; streaming data I.

Classification of streaming data faces three basic challenges: it has to deal with huge amounts of data, the varying time between two stream data items must be used best possible (anytime classification) and additional training data must be incrementally learned (anytime learning) for applying the classifier consistently to fast data streams. In this work, we propose a novel index-based technique that can handle all three of the above challenges using the established Bayes classifier on effective kernel density estimators. Our novel Bayes tree automatically generates (adapted efficiently to the individual object to be classified) a hierarchy of mixture densities that represent kernel density estimators at successively coarser levels. Our probability density queries together with novel classification improvement strategies provide the necessary information for very effective classification at any point of interruption. Moreover, we propose a novel evaluation method for anytime classification using Poisson streams and demonstrate the anytime learning performance of the Bayes tree. 1.

Abstract. Real-time data mining of high-speed and non-stationary data streams has a large potential in such fields as efficient operation of machinery and vehicles, wireless sensor networks, urban traffic control, stock data analysis etc.. These domains are characterized by a great volume of noisy, uncertain data, and restricted amount of resources (mainly computational time). Anytime algorithms offer a tradeoff between solution quality and computation time, which has proved useful in applying artificial intelligence techniques to timecritical problems. In this paper we are presenting a new, enhanced version of an anytime algorithm for constructing a classification model called Information Network (IN). The algorithm improvement is aimed at reducing its computational cost while preserving the same level of model quality. The quality of the induced model is evaluated by its classification accuracy using the standard 10-fold cross validation. The improvement in the algorithm anytime performance is demonstrated on several benchmark data streams.