Learning Patterns from Imbalanced Evolving Data Streams

Learning patterns from evolving data streams is challenging due to the characteristics of such streams: being continuous, unbounded and high speed data of non-stationary nature, which must be processed on the fly, using minimal computational resources. An additional challenge is imposed by the imbalanced data streams in many real-world applications, this difficulty becomes more prominent in multi-class learning tasks. This paper investigates the multi-class imbalance problem in non-stationary streams and develops a method to exploit realtime stream data and capture the dynamic of patterns from heterogeneous streams. In particular, we seek to extend concept drift adaptation techniques into imbalanced classes’ scenarios, and accordingly, we use an adaptive learner to classify multiple streams over a sequence of titled time windows. We include examples of the falsely classified instances in the training set, then we propose using a dynamic support threshold to discover the frequent patterns in these streams. We conduct an experiment on the car parking lots environment of a typical University with three simulated streams from sensors, smart pay stations and a mobile application. The result indicates the efficiency of applying adaptive learner approaches and modifying the training set to cope with the concept drift in multi-class imbalance scenarios, it also shows the merit of using a dynamic threshold to detect the rare patterns from evolving streams

Low-resource learning in complex games

This project is concerned with learning to take decisions in complex domains, in games in particular. Previous work assumes that massive data resources are available for training, but aside from a few very popular games, this is generally not the case, and the state of the art in such circumstances is to rely extensively on hand-crafted heuristics. On the other hand, human players are able to quickly learn from only a handful of examples, exploiting specific characteristics of the learning problem to accelerate their learning process. Designing algorithms that function in a similar way is an open area of research and has many applications in today’s complex decision problems. One solution presented in this work is design learning algorithms that exploit the inherent structure of the game. Specifically, we take into account how the action space can be clustered into sets called types and exploit this characteristic to improve planning at decision time. Action types can also be leveraged to extract high-level strategies from a sparse corpus of human play, and this generates more realistic trajectories during planning, further improving performance. Another approach that proved successful is using an accurate model of the environment to reduce the complexity of the learning problem. Similar to how human players have an internal model of the world that allows them to focus on the relevant parts of the problem, we decouple learning to win from learning the rules of the game, thereby making supervised learning more data efficient. Finally, in order to handle partial observability that is usually encountered in complex games, we propose an extension to Monte Carlo Tree Search that plans in the Belief Markov Decision Process. We found that this algorithm doesn’t outperform the state of the art models on our chosen domain. Our error analysis indicates that the method struggles to handle the high uncertainty of the conditions required for the game to end. Furthermore, our relaxed belief model can cause rollouts in the belief space to be inaccurate, especially in complex games. We assess the proposed methods in an agent playing the highly complex board game Settlers of Catan. Building on previous research, our strongest agent combines planning at decision time with prior knowledge extracted from an available corpus of general human play; but unlike this prior work, our human corpus consists of only 60 games, as opposed to many thousands. Our agent defeats the current state of the art agent by a large margin, showing that the proposed modifications aid in exploiting general human play in highly complex games