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Title:
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COMPARATIVE STUDY OF FEATURE SELECTION METHODS TO ANALYZE PERFORMANCE OF LUNG CANCER DATA |
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Author(s):
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Emel Koc, A. Nevra Ozer |
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ISBN:
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978-989-8533-39-5 |
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Editors:
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Ajith P. Abraham, Antonio Palma dos Reis and Jörg Roth |
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Year:
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2015 |
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Edition:
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Single |
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Keywords:
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Feature Selection, Information Gain Attribute Evaluation, Chi-Squared Attribute Evaluation, Filtered Attribute Evaluation, Gain Ratio Attribute Evaluation, |
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Type:
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Poster/Demonstration |
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First Page:
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219 |
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Last Page:
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222 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Feature selection, also known as attribute selection, is a process which attempts to select more informative features among datasets to be used in model construction. The main aim of feature selection can improve the prediction accuracy and reduce the computational overhead of classification algorithms. In this study, several approaches such as Information Gain Attribute Evaluation, Chi-Squared Attr?bute Evaluation, Filtered Attribute Evaluation, Gain Ratio Attribute Evaluation and Symmetrical Uncertainty Attribute Evaluation are carried out to discover the discriminative features on the same disease, namely lung cancer, using four different medical datasets. The efficiency of each approach is evaluated using machine learning software. |
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