28,744 research outputs found
Evaluation of the impact of the indiscernibility relation on the fuzzy-rough nearest neighbours algorithm
Fuzzy rough sets are well-suited for working with vague, imprecise or
uncertain information and have been succesfully applied in real-world
classification problems. One of the prominent representatives of this theory is
fuzzy-rough nearest neighbours (FRNN), a classification algorithm based on the
classical k-nearest neighbours algorithm. The crux of FRNN is the
indiscernibility relation, which measures how similar two elements in the data
set of interest are. In this paper, we investigate the impact of this
indiscernibility relation on the performance of FRNN classification. In
addition to relations based on distance functions and kernels, we also explore
the effect of distance metric learning on FRNN for the first time. Furthermore,
we also introduce an asymmetric, class-specific relation based on the
Mahalanobis distance which uses the correlation within each class, and which
shows a significant improvement over the regular Mahalanobis distance, but is
still beaten by the Manhattan distance. Overall, the Neighbourhood Components
Analysis algorithm is found to be the best performer, trading speed for
accuracy
Rough sets theory for travel demand analysis in Malaysia
This study integrates the rough sets theory into tourism demand analysis. Originated from the area of Artificial Intelligence, the rough sets theory was introduced to disclose important structures and to classify objects. The Rough Sets methodology provides definitions and methods for finding which attributes separates one class or classification from another. Based on this theory can propose a formal framework for the automated transformation of data into knowledge. This makes the rough sets approach a useful classification and pattern recognition technique. This study introduces a new rough sets approach for deriving rules from information table of tourist in Malaysia. The induced rules were able to forecast change in demand with certain accuracy
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