Enhancing Wrapper Based Algorithms for Selecting Optimal Features from Thyroid Disease Dataset
DOI:
https://doi.org/10.26438/ijcse/v6i3.713Keywords:
Data Mining, Feature Selection, Wrapper Method, Genetic Algorithm, Ant Colony OptimizationAbstract
Advances in medical information technology have enabled healthcare industries to automatically collect huge amount of data through clinical laboratory examinations. Thyroid disease (TD) is a study of Endocrinology and is considered as one of the most common diseases that is frequently misunderstood and misdiagnosed. Machine learning techniques are increasingly introduced to construct the CAD systems owing to its strong capability of extracting complex relationships in the biomedical data. Feature selection is a technique to choose a subset of variables from the multidimensional data which can improve the classification accuracy in diversity datasets. In addition, the best feature subset selection method can reduce the cost of feature measurement. This work focuses on enhancing the wrapper based algorithms for feature selection.
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