322,974 research outputs found
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
Log-canonical pairs and Gorenstein stable surfaces with
We classify log-canonical pairs of dimension two with
an ample Cartier divisor with , giving some
applications to stable surfaces with . A rough classification is also
given in the case
A note on a separating system of rational invariants for finite dimensional generic algebras
The paper deals with a construction of a separating system of rational
invariants for finite dimensional generic algebras. In the process of dealing
an approach to a rough classification of finite dimensional algebras is offered
by attaching them some quadratic forms
A breast cancer diagnosis system: a combined approach using rough sets and probabilistic neural networks
In this paper, we present a medical decision support system based on a hybrid approach utilising rough sets and a probabilistic neural network. We utilised the ability of rough sets to perform dimensionality reduction to eliminate redundant attributes from a biomedical dataset. We then utilised a probabilistic neural network to perform supervised classification. Our results indicate that rough sets was able to reduce the number of attributes in the dataset by 67% without sacrificing classification accuracy. Our classification accuracy results yielded results on the order of 93%
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