Supervised machine learning classification algorithms assume both train and test data are sampled from the same domain or distribution. However, performance of the algorithms degrade for test data from different domain. Such cross domain classification is arduous as features in the test domain may be different and absence of labeled data could further exacerbate the problem. This paper proposes an algorithm to adapt classification model by iteratively learning domain specific features from the unlabeled test data. Moreover, this adaptation transpires in a similarity aware manner by integrating similarity between domains in the adaptation setting. Cross-domain classification experiments on different datasets, including a real world dataset, demonstrate efficacy of the proposed algorithm over state-of-theart.
The steady growth of data from social networks has resulted in wide-spread research in a host of application areas including transportation, health-care, customer-care and many more. Owing to the ubiquity and popularity of transportation (more recently) the growth in the number of problems reported by the masses has no bounds. With the advent of social media, reporting problems has become easier than before. In this paper, we address the problem of efficient management of transportation related woes by leveraging the information provided by social media sources such as -Facebook, Twitter etc. We develop techniques for viral event detection, identify frequently co-occurring problem patterns and their root-causes and mine suggestions to solve the identified problems. We predict the occurrence of different problems, (with an accuracy of ≈ 80%) at different locations and times leveraging the analysis done above along with weather information and news reports. In addition, we design a feature-packed visualization that significantly enhances the ability to analyse data in real-time.
We demonstrate SODA (Service Oriented Domain Adaptation) for efficient and scalable cross-domain microblog categorization which works on the principle of transfer learning. It is developed on a novel similarity-based iterative domain adaptation algorithm while extended with features such as active learning and interactive GUI to be used by business professionals. SODA demonstrates efficient classification accuracy on new collections while minimizing and sometimes eliminating the need for expensive data labeling efforts. SODA also implements an active learning (AL) technique to select informative instances from the new collection to seek annotations, if a small amount of labeled data is required by the adaptation algorithm.
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