TY - GEN
T1 - The performance of one dimensional Naïve Bayes Classifier for Feature Selection in Predicting Prospective Car Insurance Buyers
AU - Salma, Dilla Fadlillah
AU - Murfi, Hendri
AU - Sarwinda, Devvi
N1 - Publisher Copyright:
© 2019, Springer Nature Singapore Pte Ltd.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - One of the products sold by insurance companies is car insurance. To offer this product, one of the techniques used by the company is cold calling. This method often decreases the sellers' mentalities because they face many rejections when offering insurance products. This problem can be reduced by classifying prospective buyers' data first. The data can be classified as customers with the potential to buy insurance and customers who have no potential to buy insurance. From the obtained data, there are certainly many features that support the classification process. However, not all features contributed to improving classification accuracy. Machine learning especially the method of feature selection helps to reduce dimensions and to improve classification accuracy. In this paper, we examine One-Dimensional Naïve Bayes Classifier (1-DBC) as a feature selection method that is applied to two classifier methods, i.e., Support Vector Machine and Logistic Regression. Our simulations show that the two classifiers can use fewer features to produce comparable accuracies in classifying prospective car insurance buyers.
AB - One of the products sold by insurance companies is car insurance. To offer this product, one of the techniques used by the company is cold calling. This method often decreases the sellers' mentalities because they face many rejections when offering insurance products. This problem can be reduced by classifying prospective buyers' data first. The data can be classified as customers with the potential to buy insurance and customers who have no potential to buy insurance. From the obtained data, there are certainly many features that support the classification process. However, not all features contributed to improving classification accuracy. Machine learning especially the method of feature selection helps to reduce dimensions and to improve classification accuracy. In this paper, we examine One-Dimensional Naïve Bayes Classifier (1-DBC) as a feature selection method that is applied to two classifier methods, i.e., Support Vector Machine and Logistic Regression. Our simulations show that the two classifiers can use fewer features to produce comparable accuracies in classifying prospective car insurance buyers.
KW - Car insurance
KW - Feature selection
KW - Logistic regression
KW - One Dimensional Naïve Bayes Classifier
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85069985594&partnerID=8YFLogxK
U2 - 10.1007/978-981-32-9563-6_13
DO - 10.1007/978-981-32-9563-6_13
M3 - Conference contribution
AN - SCOPUS:85069985594
SN - 9789813295629
T3 - Communications in Computer and Information Science
SP - 124
EP - 132
BT - Data Mining and Big Data - 4th International Conference, DMBD 2019, Proceedings
A2 - Shi, Yuhui
A2 - Tan, Ying
PB - Springer Verlag
T2 - 4th International Conference on Data Mining and Big Data, DMBD 2019
Y2 - 26 July 2019 through 30 July 2019
ER -