Proceedings of the first international workshop on Handling concept drift in adaptive information systems : importance, challenges and solutions, HaCDAIS 2010, Barcelona, Spain, September 24, 2010, held in conjunction with ECML/PKDD 2010

In the real world data is often non stationary. In predictive analytics, machine learning and data mining the phenomenon of unexpected change in underlying data over time is known as concept drift. Changes in underlying data might occur due to changing personal interests, changes in population, adversary activities or they can be attributed to a complex nature of the environment. When there is a shift in data, the predictions might become less accurate as the time passes or opportunities to improve the accuracy might be missed. Thus the learning models need to be adaptive to the changes. The problem of concept drift is of increasing importance to machine learning and data mining as more and more data is organized in the form of data streams rather than static databases, and it is rather unusual that concepts and data distributions stay stable over a long period of time. It is not surprising that the problem of concept drift has been studied in several research communities including but not limited to machine learning and data mining, data streams, information retrieval, and recommender systems. Different approaches for detecting and handling concept drift have been proposed in the literature, and many of them have already proved their potential in a wide range of application domains, e.g. fraud detection, adaptive system control, user modeling, information retrieval, text mining, biomedicine. HaCDAIS 2010 workshop1 organized in conjunction with ECML/PKDD 2010 and held on 24 September 2010 in Barcelona, Spain provides a focused international forum for researchers to discuss new, we aim to attract researchers with an interest in handling concept drift and recurring contexts in adaptive information systems. Topics discussed at the workshop include classification and clustering on data streams and evolving data, change and novelty detection in online, semi-online and offline settings, adaptive ensembles, adaptive sampling and instance selection, incremental learning and model adaptivity, delayed labeling in data streams, dynamic feature selection, handling local and complex concept drift, qualitative and quantitative evaluation of concept drift handling performance, reoccurring contexts and context-aware approaches, application-specific and domain driven approaches within the areas of information retrieval, recommender systems, pattern recognition, user modeling, decision support and adaptive information systems These proceedings include abstract of the invited talk, invited software report and six peer-reviewed papers accepted to the workshop, two as full papers and four as short papers