Learning with imbalanced datasets has been a major topic of study for many years. In this paper, we focus on a type of imbalance called imbalance due to rare instances. Such imbalances occur in a variety of domains. Rare instances have received less focus in prediction problems and we wish to draw attention to how accuracy can be improved in the presence of rare data. We discuss an approach to regression tasks, where the training instances are first grouped by similarity and a group of heterogeneous models is applied to each of these groups. This approach enables better prediction on unseen or rare instances when compared to existing approaches. We present results that show performance across datasets from different domains. Our approach was found to provide better prediction than common approaches on rare unseen instances without affecting the overall performance on prediction tasks.

In this paper, we present a complete design framework for an adaptive multiple agent fuzzy constraint-based controller (MAFCC) based on fuzzy penumbra constraint processing in each fuzzy constraint subnetwork collaborating with a connected constraint network and its corresponding semantic modeling in a first-order predicate calculus (FOPC) language, with application to a complex hydraulic system. The concept of “multiple agent” and “fuzzy constraint subnetwork” in a complex control system is introduced and some basic definitions of penumbra fuzzy constraint processing in a constraint subnetwork and the collaboration with an overall connected constraint network and its semantic modeling are addressed. As a result, a human agent interacts with system agents and allows the constraints to be added or deleted on-line according to the constraints imposed from the outside environment. Near-optimal system performance is accomplished by restricting all the penumbra constraints to be satisfied in each constraint subnetwork simultaneously which are interconnected as a result of constraints that exist between each of them. Following the principle of constraint satisfaction and fuzzy local propagation reasoning, each individual system agent is now constrained to behave in a certain fashion as dictated by the overall constraint network. In addition, the constraint network in MAFCC system provides an update strategy which makes a real time adaptive hydraulic control for all 20 cities possible.

A complete design framework for a fuzzy constraint-based controller based on fuzzy-constraint processing and its semantics and relationship to fuzzy logic is presented. In this paper, the concept of "fuzzy constraints" in problem solving is introduced, and some basic definitions of fuzzy-constraint processing in a constraint network and its semantic modeling are addressed. Then a fuzzy local propagation inference mechanism for reasoning about imprecise information applying the filter operation in a network of constraints is proposed. Moreover, we advance the concurrent fuzzy-logic controller (FLC) to a new type of controller, the fuzzy constraint-based controller (FCC), using a more general predicate calculus and full first-order logic knowledge representation and making use of the idea of fuzzy-constraint processing to model practical dynamic control systems. Finally, simulation results show that a FCC achieves equivalent performance as PD type and PI type FLCs and it also demonstrates superior outcomes to a conventional PID controller in terms of rise time and peak-percent overshoot.

A technique is discussed for both representing and automatically synthesizing high-level multirobot plans through the use of generalized condition/event (GC/E) nets. An example is given of the functionality of the approach for representing and solving problems of resource allocation in environments containing more than one agent. An important property of nets called persistence, which is essential to multiagent plans, is identified. Persistence, of allowed suborderings of events must be preserved by any net-growing algorithm that produces a plan involving nondeterministic mutual exclusion.>