Dimensionality reduction using genetic algorithm and fuzzy-rough concepts

Real-world datasets are often vague and redundant, creating problem to take decision accurately. Very recently, Rough-set theory has been used successfully for dimensionality reduction but is applicable only on discrete dataset. Discretisation of data leads to information loss and may add inconsistency in the datasets. The paper aims at developing an algorithm using fuzzy-rough concept to overcome this situation. By this approach, dimensionality of the dataset has been reduced and using genetic algorithm, an optimal subset of attributes is obtained, sufficient to classify the objects. The proposed algorithm reduces dimensionality to a great extent without degrading the accuracy of classification and avoid of being trapped at local minima. Results are compared with the existing algorithms demonstrate compatible outcome.