Mining multi-dimensional data for decision support

被引：16

作者：

Donato, JM

Schryver, JC

Hinkel, GC

Schmoyer, RL

Leuze, MR ^{[1
]}

Grandy, NW

机构：

[1] Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA

[2] Joint Inst Computat Sci, Knoxville, TN 37996 USA

来源：

FUTURE GENERATION COMPUTER SYSTEMS | 1999年 / 15卷 / 03期

关键词：

data mining; personal bankruptcy; decision trees; partially recurrent neural networks;

D O I：

10.1016/S0167-739X(98)00086-7

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Personal bankruptcy is an increasingly common yet little understood phenomenon. Attempts to predict bankruptcy have involved the application of data mining techniques to credit card data. This is a difficult problem, since credit card data is multi-dimensional, consisting of monthly account records and daily transaction records. In this paper, we describe a two-stage approach that combines decision trees and neural networks to predict personal bankruptcy using credit card data. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：433 / 441

页数：9

共 7 条

[1]

Bishop M.M., 1975, DISCRETE MULTIVARIAT

[2] FINDING STRUCTURE IN TIME [J].

ELMAN, JL .

COGNITIVE SCIENCE, 1990, 14 (02) :179-211

[3]

Kohavi R., 1996, TOOLS ARTIFICIAL INT, P234

[4] ON THE COMPUTATIONAL POWER OF ELMAN-STYLE RECURRENT NETWORKS [J].

KREMER, SC .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1995, 6 (04) :1000-1004

[5] LOCALLY RECURRENT GLOBALLY FEEDFORWARD NETWORKS - A CRITICAL-REVIEW OF ARCHITECTURES [J].

TSOI, AC ;

BACK, AD .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1994, 5 (02) :229-239

[6]

*U CAL, MACH LEARN DAT REP

[7]

ZELL A, 1995, 695 U STUTTG

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