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Genetic Algorithm And Fuzzy-Rough Based Dimensionality Reduction Applied On Real Valued Dataset

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. Discreti- sation of data leads to information loss and may add inconsis- tency in the datasets. The paper aims at applying fuzzy-rough concept to overcome the above limitations. However, handling of non discretized values increases computational complexity of the system. Therefore, to build an efficient classifier Genetic Algorithm (GA) has been applied to obtain optimal subset of attributes, sufficient to classify the objects. The proposed algo- rithm reduces dimensionality to a great extent without degrad- ing the accuracy of classification and avoid of being trapped at local minima. Results are compared with the existing algo- rithms, demonstrate compatible outcome.