Mutual Information 이론을 활용한 적정 변수선택법 적용 연구
Applying a Mutual Information Theory Based Feature Selection Method to a Classifier
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Abstract
Objective: The purpose of this study was to explore the usability of a feature selection method based on the mutual information theory to increase predictive performance of a classifier in data mining. Methods: The HIV Cost and Services Utilization Study(HCSUS) dataset was used to apply the feature selection method to a classifier. Its contribution to increasing the predictive performance of the classifier was evaluated by comparing the Naive Bayes(NB) and the Logistic Regression(LG) models using different variables. The infrequent office visit representing limited health service utilization was selected as an outcome variable. HUGIN ResearcherTM 6.3 was used to train and test the NB models and SAS 8.0 was used for the LG modeling. Results: Higher AUC in the NB model was obtained using the variables selected by the mutual information based feature selection method(AUC=.639, CI=.611, .660); lower AUC using the variables defined by a previous study(AUC=.599, CI=.570, .620). There was no difference between the LG models with different variables. Conclusion: This study demonstrated the mutual information method may be useful in identifying relevant predictors as the feature selection method, which can contribute to an increase in the predictive performance of a classifier. (Journal of Korean Society of Medical Informatics 11-3,247-253, 2005)
참고문헌 (12)
- Weiss SM, "Predictive data mining: A practical guide.", pp.81~118 (1998), Morgan Kaufmann publishers
- Cover TM, "Elements of information theory", pp.12~22 (1991)
- Shapiro MF, "Variations in the care of HIV-infected adults in the United States:results from the HIV cost and services utilization study", 281(24), pp.2305~2315 (1999)
- Burnside E, "A Bayesian network for mammography" (2000)
- Andersen RM, "Introduction to health services", pp.87~109 (1999), Delmar Publisher
- Aday LA, "Equity of access to medical care:a conceptual and empirical overview", 19(12), pp.4~27 (1981)
- Aday LA, "Indicators and predictors of health services utilization Introduction to health services", pp.46~70 (1993), Delmar Publisher Inc
- Aday LA, "Handbook of health behavior research I", pp.153~172 (1997), Health services utilization models Plenum Press
- .Rowland T,, "Comparison of multiple prediction models for ambulation following spinal cord injury", oh(-machado l), pp.17~21 (1998)
- Hosmer DW, "Goodness of fit tests for the multiple logistic regression model", a9(10), pp.1043~1069 (1980)
- Domingos P, "Conditions for the optimality of the simple Bayesian Classifier Proceeding of the Thirteenth International Conference on Machine Learning 1996", pp.105~112
- Zadrozny B, "Transforming classifier score into accurate multiclass probability estimates Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining", 24(26) (2002)