Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

Performing Feature Selection with ACO

Summary. The main aim of feature selection is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In real world problems FS is a must due to the abundance of noisy, irrelevant or misleading features. However, current methods are inadequate at finding optimal reductions. This chapter presents a feature selection mechanism based on Ant Colony Optimization in an attempt to combat this. The method is then applied to the problem of finding optimal feature subsets in the fuzzy-rough data reduction process. The present work is applied to two very different challenging tasks, namely web classification and complex systems monitoring.

Ant Colony Optimization with Three Stages for Independent Test Cost Attribute Reduction

Minimal test cost attribute reduction is an important problem in cost-sensitive learning. Recently, heuristic algorithms including the information gain-based algorithm and the genetic algorithm have been designed for this problem. However, in many cases these algorithms cannot find the optimal solution. In this paper, we develop an ant colony optimization algorithm to tackle this problem. The attribute set is represented as a graph with each vertex corresponding to an attribute and weight of each edge to pheromone. Our algorithm contains three stages, namely, the addition stage, the deletion stage, and the filtration stage. In the addition stage, each ant starts from the initial position and traverses edges probabilistically until the stopping criterion is satisfied. The pheromone of the traveled path is also updated in this process. In the deletion stage, each ant deletes redundant attributes. Two strategies, called the centralized deletion strategy and the distributed deletion strategy, are proposed. Finally, the ant with minimal test cost is selected to construct the reduct in the filtration stage. Experimental results on UCI datasets indicate that the algorithm is significantly better than the information gain-based one. It also outperforms the genetic algorithm on medium-sized dataset Mushroom