Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
권호 표지

A Study on the Combined Decision Tree(C4.5) and Neural Network Algorithm for Classification of Mobile Telecommunication Customer

이동통신고객 분류를 위한 의사결정나무(C4.5)와 신경망 결합 알고리즘에 관한 연구

  • 이극노 (고려대학교 산업시스템정보공학과) ;
  • 이홍철 (고려대학교 산업시스템정보공학과)
  • Published : 2003.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents the new methodology of analyzing and classifying patterns of customers in mobile telecommunication market to enhance the performance of predicting the credit information based on the decision tree and neural network. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship and makes special management on the customer who has huh potential of getting out of contract in advance. The real implementation of proposed method shows that the predicted accuracy is higher than existing methods such as decision tree(CART, C4.5), regression, neural network and combined model(CART and NN).

본 논문은 결합된 의사결정 나무(C4.5)와 신경망기법을 적용함으로써 고객의 신용에 대한 예측을 높이기 위하여 이동통신 고객의 패턴을 분류하고, 분석하는 새로운 방법에 대하여 연구하였다. 의사 결정나무(C4.5)를 형성하여 선택된 결정변수와 함께 규칙을 생성함으로써, 신경망의 입력벡터 값을 정의하는 체계적인 방법을 제시하였다. 고객 관리측면에서 본 논문은 이동 통신 회사의 기존고객을 분류하여 패턴을 분석함으로써 우수한 고객의 지속적인 관리와 이탈 가능성이 많은 고객을 차별 관리하여 기업이익을 증대시킬 수 있을 것이다. 또한 이러한 분류를 통하여 신규 고객에 반영함으로써 고객의 향후 관리에도 기여할 수 있을 것이다. 실제 이동통신 고객데이터를 중심으로 연구의 결과는 예측의 정확도가 기존의 의사결정 트리 모델 (CART, C4.5), 회귀모형, 신경망 접근 방법과 기존에 연구되었던 결합모델(CART ＆ 신경망)보다 훨씬 높게 연구되었다.

Keywords

References

  1. 데이터마이닝 이론과 실습 최국렬(외) 8인공저
  2. SAS Enterprise Miner 4.0을 이용한 데이터마이닝 방법론 및 활용 3판 최종후(외) 5인 공저
  3. User Interfaces to Data Intensive Systems Data mining with C4.5 and interactive cartographic visualization Andrienko,G.L.;N.V.Andrienko
  4. Signal Processing Proceedings, 1998. ICSP '98. 1998 Fourth International Conference on v.1 High speed neural network based classifier for real-time application Chung,Y.Y.;M.T.Wong;N.W.Bergmann
  5. 2002 CEC '02. Proceedings of the 2002 Congress on Evaolutionary Computation v.2 Generation of Comprehensible Decision Trees Through Evolution of Training Data Endou,T.;Q.Zhao
  6. IEEE - Computer v.32 no.Issue: 8 Mining very large databases Ganti,V.;J.Gehrde;R Ramakrishnan
  7. IT Professional v.3 no.Issue : 5 Using OLAP and multidimensional data for decision marking Hasan,H.;P.Hyland
  8. Proceedings of the International Conference in Pattern Recognition v.1 C4.5 Decision Forests Ho,T.K.
  9. Neural Networks, 2001.Proceedings.IJCNN '01.International Joint Conference on v.3 Decision Tree Pruning Using Backpropagation Neural Networks Kijsirikul,B.;K.Chongkasemwongse
  10. IFSA World Congress and 20th NAFIPS International Conference v.2 Mining the customer credit by using the neural network model with classification and regression tree approach Kao.L.J.;C.C.Chiu
  11. IEEE Transactions on Knowledge and Data Engineering v.8 no.Issue : 6 Effective data mining using neural networks Lu,H.;R.Setiono;H.Liu
  12. Intelligent Systems in Accounting, Finance and Management v.6 Prediction Bond Ratings Using Neural Networks: a Comparison with Logistic Regression Maher,J.J;T.K.Sen
  13. IEEE Transactions on Neural Networks v.13 no.Issue : 1 Data mining in soft compution framework:a survey Mitra,S.;S.K.Pal;P.Mitra
  14. Ecol. Modell v.98 Prediction of Functional Characteristics of ecosystems: a comparison of artificial neural networks and regression models Paruelo,J.M.;F.Tomasel
  15. C4.5: Programs for Machine Learning Quinlan,J.R.
  16. Machine Intelligence v.11 Decision Trees and Multi-valued attributes Quinlan,J.R.
  17. IEEE Transactions on Knowledge and Data Engineering v.14 no.Issue: 2 Efficient C4.5 Ruggieri,S
  18. IEE Transactions on Neural Networks v.10 no.6 ANN-DT: An Algorithm for Extraction of Decision Tress from Artificial Neural Networks Schmitz,G.P.J.;C.Aldrich;F.S.Gouws
  19. Proceedings of the IEEE v.78 no.Issue: 10 Entropy nets: from decision tress to neural networks Sethi,I.K
  20. Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on v.4 Hybrid NN-DT Cascade Method For Generating Decision Trees From Backpropagation Neural Networks Zorman,M.;P.Kokol