دانلود گزارش کارورزی و کار آموزی

چکیده : 

Attribute selection is one of the important problems encountered in pattern recognition, machine learning,

data mining, and bioinformatics. It refers to the problem of selecting those input attributes or features

that are most effective to predict the sample categories. In this regard, rough set theory has been shown

to be successful for selecting relevant and nonredundant attributes from a given data set. However, the

classical rough sets are unable to handle real valued noisy features. This problem can be addressed by

the fuzzy-rough sets, which are the generalization of classical rough sets. A feature selection method is

presented here based on fuzzy-rough sets by maximizing both relevance and significance of the selected

features. This paper also presents different feature evaluation criteria such as dependency, relevance,

redundancy, and significance for attribute selection task using fuzzy-rough sets. The performance of different

rough set models is compared with that of some existing feature evaluation indices based on the

predictive accuracy of nearest neighbor rule, support vector machine, and decision tree. The effectiveness

of the fuzzy-rough set based attribute selection method, along with a comparison with existing feature

evaluation indices and different rough set models, is demonstrated on a set of benchmark and microarray