extracts the second least numbers of features (i.e. the second lowest extraction rate). In other words, DT is a good feature selection method that can extract the better informative variables to increase the accuracy of prediction models and decrease the time for training & testing (i.e. about 6 min.). This method improves not only effectiveness but also efficiency successfully. In short, although all of these six prediction models perform very similar in terms of prediction accuracy and error rates, using a smaller number of features can efficiently construct a prediction model. Therefore, considering both effectiveness and efficiency results, DT outperforms the others. In considering the ANOVA results to have a high level of significance difference, we can rank these feature selection methods not shown in the paper. DT is the best feature selection method to provide the highest rate of prediction accuracy and the lowest rate of Type I errors. STEPWISE is another method which provides relatively better performances in prediction accuracy and Type II errors. However, since it selects more features (i.e. the extraction rate is 58.10%), the average time for training & testing is larger than DT, AR, and PCA. Multiple Feature Selection Methods Table 6 shows the prediction performances of using six multiple feature selection methods. These include the intersection of GA and STEPWISE, the union of DT and PCA, the union of AR, DT, and PCA, the union of AR and DT, the union of AR and PCA, and the intersection of PCA and STEPWISE respectively. Table 6 Performances of multiple feature selection methods unit: % No. of Avg. Avg. Time Extraction Type I Type II Method selected Accuracy for training rate (%) error error features (%) & testing 26 42.6 75.06 43.08 15.95 38 min. GA∩STEPWISE DT∪PCA 22 36.1 74.75 42.53 16.14 29 min. AR∪DT∪PCA 26 42.6 74.45 42.87 16.99 38 min. AR∪DT 12 19.7 74.34 44.13 16.54 12 min. AR∪PCA 21 34.4 73.86 45.55 16.55 25 min. 10 16.4 73.53 42.68 18.46 min. PCA∩STEPWISE
extracts the second least numbers of features (i.e. the second lowest extraction rate). In other words, DT is a good feature selection method that can extract the better informative variables to increase the accuracy of prediction models and decrease the time for training & testing (i.e. about 6 min.). This method improves not only effectiveness but also efficiency successfully. In short, although all of these six prediction models perform very similar in terms of prediction accuracy and error rates, using a smaller number of features can efficiently construct a prediction model. Therefore, considering both effectiveness and efficiency results, DT outperforms the others. In considering the ANOVA results to have a high level of significance difference, we can rank these feature selection methods not shown in the paper. DT is the best feature selection method to provide the highest rate of prediction accuracy and the lowest rate of Type I errors. STEPWISE is another method which provides relatively better performances in prediction accuracy and Type II errors. However, since it selects more features (i.e. the extraction rate is 58.10%), the average time for training & testing is larger than DT, AR, and PCA. Multiple Feature Selection Methods Table 6 shows the prediction performances of using six multiple feature selection methods. These include the intersection of GA and STEPWISE, the union of DT and PCA, the union of AR, DT, and PCA, the union of AR and DT, the union of AR and PCA, and the intersection of PCA and STEPWISE respectively. Table 6 Performances of multiple feature selection methods unit: % Method No. of selected features Extraction rate (%) Avg. Accuracy (%) Type I error Type II error Avg. Time for training & testing GA∩STEPWISE 26 42.6 75.06 43.08 15.95 38 min. DT∪PCA 22 36.1 74.75 42.53 16.14 29 min. AR∪DT∪PCA 26 42.6 74.45 42.87 16.99 38 min. AR∪DT 12 19.7 74.34 44.13 16.54 12 min. AR∪PCA 21 34.4 73.86 45.55 16.55 25 min. PCA∩STEPWISE 10 16.4 73.53 42.68 18.46 min.
051-Determinants%20of%20Intangible%20Assets%20The%20Application%20of%20Data%20Mining%20Technologies.docx