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An Uncertainty-Based Model for Optimized Multi-Label Classification
Abstract
The data used in the real world applications are uncertain and vague. Several models to handle such data efficiently have been put forth so far. It has been found that the individual models have some strong points and certain weak points. Efforts have been made to combine these models so that the hybrid models will cash upon the strong points of the constituent models. Dubois and Prade in 1990 combined rough set and fuzzy set together to develop two models of which rough fuzzy model is a popular one and is used in many fields to handle uncertainty-based data sets very well. Particle Swarm Optimization (PSO) further combined with the rough fuzzy model is expected to produce optimized solutions. Similarly, multi-label classification in the context of data mining deals with situations where an object or a set of objects can be assigned to multiple classes. In this chapter, the authors present a rough fuzzy PSO algorithm that performs classification of multi-label data sets, and through experimental analysis, its efficiency and superiority has been established.
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