Back Room Front Room 2

More documents

Recommendations

Info

COMPREHENSIBLE CREDIT-SCORING KNOWLEDGE VISUALIZATION USING DECISION TABLES AND DIAGRAMS Christophe Mues, Johan Huysmans, Jan Vanthienen K.U.Leuven Naamsestraat 69, B-3000 Leuven, Belgium Email: {Christophe.Mues; Johan.Huysmans; Jan.Vanthienen}@econ.kuleuven.ac.be Bart Baesens University of Southampton School of Management, SO17 1BJ Southampton, UK Email: B.M.M.Baesens@soton.ac.uk Keywords: Credit scoring, neural network rule extraction, decision tables, decision diagrams. Abstract: One of the key decision activities in financial institutions is to assess the credit-worthiness of an applicant for a loan, and thereupon decide whether or not to grant the loan. Many classification methods have been suggested in the credit-scoring literature to distinguish good payers from bad payers. Especially neural networks have received a lot of attention. However, a major drawback is their lack of transparency. While they can achieve a high predictive accuracy rate, the reasoning behind how they reach their decisions is not readily available, which hinders their acceptance by practitioners. Therefore, we have, in earlier work, proposed a two-step process to open the neural network black box which involves: (1) extracting rules from the network; (2) visualizing this rule set using an intuitive graphical representation. In this paper, we will focus on the second step and further investigate the use of two types of representations: decision tables and diagrams. The former are a well-known representation originally used as a programming technique. The latter are a generalization of decision trees taking on the form of a rooted, acyclic digraph instead of a tree, and have mainly been studied and applied by the hardware design community. We will compare both representations in terms of their ability to compactly represent the decision knowledge extracted from two real-life credit-scoring data sets. 1 INTRODUCTION One of the key decisions financial institutions have to make as part of their daily operations is to decide whether or not to grant a loan to an applicant. With the emergence of large-scale data-storing facilities, huge amounts of data have been stored regarding the repayment behavior of past applicants. It is the aim of credit scoring to analyze this data and build data-mining models that distinguish good applicants from bad applicants using characteristics such as amount on savings account, marital status, purpose of loan, etc. Many machine-learning and statistical techniques have been suggested in the literature to build credit-scoring models (Baesens et al., 2003c; Thomas, 2000). Amongst the most popular are traditional statistical methods (e.g. logistic regression (Steenackers and Goovaerts, 1989)), nonparametric statistical models (e.g. k-nearest neighbor (Henley and Hand, 1997) and classification trees (David et al., 1992)) and neural networks (Baesens et al., 2003b). However, when looking at today’s credit-scoring practice, one typically sees that the estimated classification models, although often based on advanced I. Seruca et al. (eds.), Enterprise Information Systems VI, © 2006 Springer. Printed in the Netherlands. 109 109–115. and powerful algorithms, fail to be successfully integrated into the credit decision environment. One of the key underlying reasons for this problem, is that the extracted knowledge and patterns can not easily be represented in a way that facilitates human interpretation and validation. Hence, properly visualizing the knowledge and patterns extracted by a data-mining algorithm is becoming more and more a critical success factor for the development of decision-support systems for credit scoring. Therefore, in this paper, we report on the use of different knowledge visualization formalisms for credit scoring. Starting from a set of propositional if-then rules previously extracted by a powerful neural network rule extraction algorithm, we will investigate both decision tables and decision diagrams as alternative knowledge visualization schemes. The latter are a generalization of decision trees taking on the form of a rooted, acyclic digraph instead of a tree, and have mainly been studied and applied by the hardware design community. We will compare both representations in terms of their ability to compactly represent the decision knowledge extracted from two real-life credit-scoring data sets.
110 ENTERPRISE INFORMATION SYSTEMS VI This paper is organized as follows. Section 2 discusses the basic concepts of decision tables. Section 3 then elaborates on how decision diagrams may provide an alternative, more concise view of the extracted patterns. Empirical results are presented in section 4. Section 5 concludes the paper. 2 DECISION TABLES Decision tables (DTs) are a tabular representation used to describe and analyze decision situations (e.g. credit-risk evaluation), where the state of a number of conditions jointly determines the execution of a set of actions. A DT consists of four quadrants, separated by double-lines, both horizontally and vertically. The horizontal line divides the table into a condition part (above) and an action part (below). The vertical line separates subjects (left) from entries (right). The condition subjects are the criteria that are relevant to the decision-making process. They represent the attributes of the rule antecedents about which information is needed to classify a given applicant as good or bad. The action subjects describe the possible outcomes of the decision-making process (i.e., the classes of the classification problem: applicant = good or bad). Each condition entry describes a relevant subset of values (called a state) for a given condition subject (attribute), or contains a dash symbol (‘-’) if its value is irrelevant within the context of that column (‘don’t care’ entry). Subsequently, every action entry holds a value assigned to the corresponding action subject (class). True, false and unknown action values are typically abbreviated by ‘×’, ‘-’, and ‘.’, respectively. Every column in the entry part of the DT thus comprises a classification rule, indicating what action(s) apply to a certain combination of condition states. E.g., in Figure 1 (b), the final column tells us to classify the applicant as good if owns property = no, and savings amount = high. If each column only contains simple states (no contracted or don’t care entries), the table is called an expanded DT, whereas otherwise the table is called a contracted DT. Table contraction can be achieved by combining logically adjacent (groups of) columns that lead to the same action configuration. For ease of legibility, we will allow only contractions that maintain a lexicographical column ordering, i.e., in which the entries at lower rows alternate before the entries above them; see Figure 1 (Figure 2) for an example of an (un)ordered DT, respectively. As a result of this ordering restriction, a decision tree structure emerges in the condition entry part of the DT, which lends itself very well to a top-down evaluation procedure: starting at the first row, and then working one’s way down the table by choosing from the relevant condition states, one safely arrives at the prescribed action (class) for a given case. The number of columns in the contracted table can be further minimized by changing the order of the condition rows. It is obvious that a DT with a minimal number of columns is to be preferred since it provides a more parsimonious and comprehensible representation of the extracted knowledge than an expanded DT (see Figure 1). 1. Owns property? yes no 2. Years client ≤ 3 >3 ≤ 3 >3 3. Savings amount low high low high low high low high 1. Applicant=good - × × × - × - × 2. Applicant=bad × - - - × - × - (a) Expanded DT 1. Owns property? yes no 2. Years client ≤ 3 >3 - 3. Savings amount low high - low high 1. Applicant=good - × × - × 2. Applicant=bad × - - × - (b) Contracted DT 1. Savings amount low high 2. Owns property? yes no - 3. Years client ≤ 3 >3 - - 1. Applicant=good - × - × 2. Applicant=bad × - × - (c) Minimum-size contracted DT Figure 1: Minimizing the number of columns of a lexicographically ordered DT 1. Savings amount high - low low 2. Owns property? - yes no - 3. Years client - >3 - ≤ 3 1. Applicant=good × × - - 2. Applicant=bad - - × × Figure 2: Example of an unordered DT Note that we deliberately restrict ourselves to single-hit tables, wherein columns have to be mutually exclusive, because of their advantages with respect to verification and validation (Vanthienen et al., 1998). It is this type of DT that can be easily checked for potential anomalies, such as inconsistencies (a particular case being assigned to more than one class) or incompleteness (no class assigned). The decision table formalism thus facilitates the expert’s assessment of the knowledge extracted by e.g. a neural network rule extraction algorithm. What’s more, consulting a DT in a top-down manner, as suggested above, should prove more intuitive, faster, and less prone to human error, than evaluating a set of rules one by one. 3 DECISION DIAGRAMS Decision diagrams are a graph-based representation of discrete functions, accompanied by a set of graph algorithms that implement operations on these func-
Page 1 and 2:
www.dbeBooks.com - An Ebook Library
Page 3 and 4:
A C.I.P. Catalogue record for this
Page 5 and 6:
vi TABLE OF CONTENTS PART 1 - DATAB
Page 7 and 8:
viii TABLE OF CONTENTS A WIRELESS A
Page 9 and 10:
CONFERENCE COMMITTEE Honorary Presi
Page 11 and 12:
Hung, P. (AUSTRALIA) Jahankhani, H.
Page 13 and 14:
Invited Papers
Page 15 and 16:
2 ENTERPRISE INFORMATION SYSTEMS VI
Page 17 and 18:
Page 19 and 20:
Page 21 and 22:
Page 23 and 24:
10 ENTERPRISE INFORMATION SYSTEMS V
Page 25 and 26:
Page 27 and 28:
Page 29 and 30:
Page 31 and 32:
Page 33 and 34:
Page 35 and 36:
Page 37 and 38:
EVOLUTIONARY PROJECT MANAGEMENT: MU
Page 39 and 40:
Page 41 and 42:
Page 43 and 44:
MANAGING COMPLEXITY OF ENTERPRISE I
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
ASSESSING EFFORT PREDICTION MODELS
Page 69 and 70: ASSESSING EFFORT PREDICTION MODELS
Page 75 and 76: ORGANIZATIONAL AND TECHNOLOGICAL CR
Page 79 and 80: ASAP Processes W Project Kickoff A
Page 83 and 84: since it means changing the relevan
Page 85 and 86: ACME-DB: AN ADAPTIVE CACHING MECHAN
Page 87 and 88: ACME-DB: AN ADAPTIVE CACHING MECHAN
Page 89 and 90: Hit Rate (%) ACME-DB: AN ADAPTIVE C
Page 91 and 92: 5.3.6 Effect on all Policies by Int
Page 93 and 94: ACKNOWLEDGEMENTS We would like to t
Page 95 and 96: This paper describes the data sampl
Page 97 and 98: The major limit A of the operation
Page 99 and 100: RELATIONAL SAMPLING FOR DATA QUALIT
Page 101 and 102: TOWARDS DESIGN RATIONALES OF SOFTWA
Page 103 and 104: ((Brown et al., 1998), pp. 159-190,
Page 105 and 106: sive data filtering, presentation (
Page 107 and 108: • implementation changes in servi
Page 109 and 110: MEMORY MANAGEMENT FOR LARGE SCALE D
Page 111 and 112: Data Rate [bytes/sec] 1600000 14000
Page 113 and 114: E.g., DV Camcorder Camera Packets (
Page 115 and 116: Procedure CheckOptimal (n rs 1 ...n
Page 117 and 118: MEMORY MANAGEMENT FOR LARGE SCALE D
Page 119: PART 2 Artificial Intelligence and
Page 123 and 124: 112 ENTERPRISE INFORMATION SYSTEMS
Page 127 and 128: DYNAMIC MULTI-AGENT BASED VARIETY F
Page 169 and 170: AN EXPERIENCE IN MANAGEMENT OF IMPR
Page 171 and 172:
160 ENTERPRISE INFORMATION SYSTEMS
Page 173 and 174:
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
ANALYSIS AND RE-ENGINEERING OF WEB
Page 181 and 182:
Module p0 Customer Itinerary Send I
Page 183 and 184:
departments: the marketing dept. se
Page 185 and 186:
• An acyclic module M is usable,
Page 187 and 188:
BALANCING STAKEHOLDER’S PREFERENC
Page 189 and 190:
The scenarios will provide an abstr
Page 191 and 192:
BALANCING STAKEHOLDER'S PREFERENCES
Page 193 and 194:
BALANCING STAKEHOLDER'S PREFERENCES
Page 195 and 196:
A POLYMORPHIC CONTEXT FRAME TO SUPP
Page 197 and 198:
A POLYMORPHIC CONTE XT FRAME TO SUP
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
FEATURE MATCHING IN MODEL-BASED SOF
Page 205 and 206:
combination of solution domains - a
Page 207 and 208:
• features for all the concepts:
Page 209 and 210:
Existence In both steps it is possi
Page 211 and 212:
matching strategies are maximal, mi
Page 213 and 214:
TOWARDS A META MODEL FOR DESCRIBING
Page 215 and 216:
elementary message (ae-message) can
Page 217 and 218:
problems on a pragmatic level, whic
Page 219 and 220:
TOWARDS A META MODEL FOR DESCRIBING
Page 221 and 222:
INTRUSION DETECTION SYSTEMS USING A
Page 223 and 224:
INTRUSION DETECTION SYSTEMS USING A
Page 225 and 226:
(k� 1) (k) (k�1) (k) p � w
Page 227 and 228:
Table 5: Performance Comparison of
Page 229 and 230:
A USER-CENTERED METHODOLOGY TO GENE
Page 231 and 232:
carries out the second phase of the
Page 233 and 234:
1 1 1 1 2 PROCESS STORE ENTITY EDGE
Page 235 and 236:
constraints rules. KOGGE (Ebert et
Page 237 and 238:
PART 4 Software Agents and Internet
Page 239 and 240:
Page 241 and 242:
Page 243 and 244:
Page 245 and 246:
Page 247 and 248:
Page 249 and 250:
Page 251 and 252:
Page 253 and 254:
Page 255 and 256:
Page 257 and 258:
Page 259 and 260:
Page 261 and 262:
Page 263 and 264:
Page 265 and 266:
Page 267 and 268:
Page 269 and 270:
Page 271 and 272:
Page 273 and 274:
Page 275 and 276:
Page 277 and 278:
Page 279 and 280:
Page 281 and 282:
Page 283 and 284:
Page 285 and 286:
CABA 2 L A BLISS PREDICTIVE COMPOSI
Page 287 and 288:
y disables evidencing severe mental
Page 289 and 290:
Figure 4: Symbols emission in DAR-H
Page 291 and 292:
they evidence a generalization erro
Page 293 and 294:
A METHODOLOGY FOR INTERFACE DESIGN
Page 295 and 296:
and mobility loss coupled with redu
Page 297 and 298:
Tradeoff: The default input is poss
Page 299 and 300:
Sessions on the VABS which are held
Page 301 and 302:
A CONTACT RECOMMENDER SYSTEM FOR A
Page 303 and 304:
A CONTACT RECOMMENDER SYSTEM FOR A
Page 305 and 306:
- "Going to the sources". The user
Page 307 and 308:
Here are the matrix W and P for our
Page 309 and 310:
EMOTION SYNTHESIS IN VIRTUAL ENVIRO
Page 311 and 312:
theme turns out to be surprisingly
Page 313 and 314:
6 FROM FEATURES TO SYMBOLS 6.1 Face
Page 315 and 316:
sions are described by different em
Page 317 and 318:
Electronic Imaging 2000 Conference
Page 319 and 320:
to the accessibility problem for th
Page 321 and 322:
Figure 3: Hierarchical View of the
Page 323 and 324:
4 CONCLUSIONS AND FUTURE WORK On th
Page 325 and 326:
PERSONALISED RESOURCE DISCOVERY SEA
Page 327 and 328:
Page 329 and 330:
profile, the user defines his curre
Page 331 and 332:
Page 333 and 334:
Abdelkafi, N. .....................
show all

Back Room Front Room 2

Create successful ePaper yourself

Delete template?

Save as template?