지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2011.5
- 수록면
- 304 - 310 (7page)
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초록· 키워드
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In this study customer income is predicted using U.S. census data employing data mining techniques. A comparative performance analysis of decision tree c5.0, back propagation neural networks, and linear regression models is conducted. This study also suggests the strategic implications of the results from the customer relations perspective for marketing strategy in practice and these results have various practical implications to retail banks with regards to decision making and strategic planning. This can also be applied for fraud detection of income gaps between predicted and actual incomes. The results show that decision tree c5.0 outperforms other models in predictability hence focusing more on decision tree and its advantages in lieu of prediction accuracy. In the mean time a set of decision rules are also extracted from the trained decision tree c5.0 in order to improve the clarity and explicability of the income prediction model.
목차
Abstract
I. Introduction
II. Methodologies
III. Design of Experiments and Results
V. Conclusion
References
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UCI(KEPA) : I410-ECN-0101-2015-003-001491863