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Table 1: The number of columns in the expanded and minimized DTs Data set Columns in Columns in expanded DT minimized DT German 6600 11 Bene1 192 14 For example, in the DT for Bene1 (cf. Figure 6), column blocks {2, 3, 4, 5} and {9, 10, 11, 12}, though having the same respective action values, are not eligible for contraction, because they differ in more than one condition entry (viz., with respect to the attributes ‘purpose’ and ‘term’). On the other hand, a decision diagram, which allows the sharing of one such instance through multiple incoming edges, might be smaller than the corresponding tree or table. Therefore, in addition to the DT, we have built an equivalent MDD representation based on the extracted rule set, thereby adhering to the same ordering of attributes as in the minimum-size DT. Figure 7 presents the resulting diagram for Bene1. It was produced using the Graphviz graph-drawing software (Gansner et al., 1993; AT&T, 2003). Unlike in Figure 6, the part of the MDD representation that matches the replicated table segment is included only once: the subgraph rooted at the rightmost of the two ‘years client’-nodes is effectively shared through its two incoming edges. Hence, describing the function in MDD format results in a more compact representation, because the merging rule, unlike the DT contraction rule, does apply here. This empirically confirms why we consider a decision diagram to be a valuable alternative knowledge visualization aid. Nevertheless, decision diagrams are so far seldom considered in this context, despite their being a graph-based generalization of the far more frequently applied decision tree representation. We have repeated the same exercise for the German credit data set, but in the latter case, no further size savings could be obtained vis-à-vis the DT representation. In Table 2, we have summarized the results of the MDD construction process for both data sets. Note that, because of the aforementioned relation between a decision tree and a lexicographically ordered DT, the figures in column (2) also match the number of splits appearing in the condition entry part of the corresponding DT. Consequently, the final column provides a measure of the additional size gains of MDD reduction over DT contraction (i.e., the added effect of graph sharing). Both decision tables and diagrams facilitate the development of powerful decision-support systems that can be integrated in the credit-scoring process. A DT consultation engine typically traverses the table in a top-down manner, inquiring the user about the con- COMPREHENSIBLE CREDIT-SCORING KNOWLEDGE VISUALIZATION Table 2: MDD size results Data set Intern. nodes Intern. nodes Size in MDD (1) in dec. tree (2) saving German 8 8 0% Bene1 9 12 25% dition states of every relevant condition encountered along the way. A similar decision procedure is induced when consulting the decision diagram representation, and following the proper path through the graph. Hence, both types of representations provide efficient decision schemes that allow a system implementation to ask targeted questions and neglect irrelevant inputs during the question/answer-dialog. Furthermore, given the availability of efficient condition reordering operations for both types of representations, questions can be easily postponed during this process. For example, in PROLOGA, the available answer options always include an additional ‘unknown’ option, which allows the user to (temporarily) skip the question. When that happens, the DT’s conditions are first reordered internally: moving the corresponding condition to the bottom of the order and then recontracting the DT may result in new don’t care entries being formed for it. After that, the session continues with the next question. If, at some point, a conclusion is reached regarding the DT’s actions, the former question could effectively be avoided; else, it eventually pops up again. In the Bene1 example, suppose that we are deciding on a particular applicant whose properties will eventually be found to match against the condition entries of column 12, which tells us to accept the loan. Before arriving at that conclusion, we are required to provide only 4 of the 7 inputs to make a classification decision: ‘term’, ‘owns property’ and ‘income’ successively turn out to be irrelevant for this case. If, on the other hand, we would consult the rule description shown in Figure 5, we would need to evaluate every single rule, thereby testing its antecedent until a condition is found that fails, before we may conclude that none of the rules applies and that the default class (applicant = good) must be chosen. 5 CONCLUSIONS 113 In this paper, we have shown how credit-scoring knowledge can be compactly visualized either in the form of decision tables or diagrams. For two reallife cases, it was first of all shown how a set of propositional if-then rules, extracted by a neural network rule extraction algorithm, can be represented as a decision table. The constructed decision tables
114 ENTERPRISE INFORMATION SYSTEMS VI 1. Savings Account ≤12.40 € > 12.40 € 2. Economical sector Sector C other - 3. Purpose - cash provisioning second-hand car other - 4. Term - ≤ 12 months > 12 months - 5. Years Client - ≤ 3 >3 ≤ 3 >3 ≤ 3 > 3 6. Owns Property - Yes No - - Yes No Yes No - - - 7. Income - - ≤ 719 € > 719 € - - - - - ≤ 719 € > 719 € - - - 1. Applicant=good - × × - × - × - × × - × × × 2. Applicant=bad × - - × - × - × - - × - - - 1 2 3 4 5 6 7 8 9 10 11 12 13 14 sector C 3 >12 months Term Yes >719 Euro cash provisioning
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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Page 73 and 74: ASSESSING EFFORT PREDICTION MODELS
Page 75 and 76: ORGANIZATIONAL AND TECHNOLOGICAL CR
Page 79 and 80: ASAP Processes W Project Kickoff A
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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
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Page 99 and 100: RELATIONAL SAMPLING FOR DATA QUALIT
Page 101 and 102: TOWARDS DESIGN RATIONALES OF SOFTWA
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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 and 120: PART 2 Artificial Intelligence and
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Page 127 and 128: DYNAMIC MULTI-AGENT BASED VARIETY F
Page 169 and 170: AN EXPERIENCE IN MANAGEMENT OF IMPR
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164 ENTERPRISE INFORMATION SYSTEMS
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ANALYSIS AND RE-ENGINEERING OF WEB
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Module p0 Customer Itinerary Send I
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departments: the marketing dept. se
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• An acyclic module M is usable,
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BALANCING STAKEHOLDER’S PREFERENC
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The scenarios will provide an abstr
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BALANCING STAKEHOLDER'S PREFERENCES
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BALANCING STAKEHOLDER'S PREFERENCES
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A POLYMORPHIC CONTEXT FRAME TO SUPP
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A POLYMORPHIC CONTE XT FRAME TO SUP
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FEATURE MATCHING IN MODEL-BASED SOF
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combination of solution domains - a
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• features for all the concepts:
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Existence In both steps it is possi
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matching strategies are maximal, mi
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TOWARDS A META MODEL FOR DESCRIBING
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elementary message (ae-message) can
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problems on a pragmatic level, whic
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TOWARDS A META MODEL FOR DESCRIBING
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INTRUSION DETECTION SYSTEMS USING A
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INTRUSION DETECTION SYSTEMS USING A
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(k� 1) (k) (k�1) (k) p � w
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Table 5: Performance Comparison of
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A USER-CENTERED METHODOLOGY TO GENE
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carries out the second phase of the
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1 1 1 1 2 PROCESS STORE ENTITY EDGE
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constraints rules. KOGGE (Ebert et
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PART 4 Software Agents and Internet
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CABA 2 L A BLISS PREDICTIVE COMPOSI
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y disables evidencing severe mental
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Figure 4: Symbols emission in DAR-H
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they evidence a generalization erro
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A METHODOLOGY FOR INTERFACE DESIGN
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and mobility loss coupled with redu
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Tradeoff: The default input is poss
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Sessions on the VABS which are held
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A CONTACT RECOMMENDER SYSTEM FOR A
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A CONTACT RECOMMENDER SYSTEM FOR A
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- "Going to the sources". The user
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Here are the matrix W and P for our
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EMOTION SYNTHESIS IN VIRTUAL ENVIRO
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theme turns out to be surprisingly
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6 FROM FEATURES TO SYMBOLS 6.1 Face
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sions are described by different em
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Electronic Imaging 2000 Conference
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to the accessibility problem for th
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Figure 3: Hierarchical View of the
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4 CONCLUSIONS AND FUTURE WORK On th
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PERSONALISED RESOURCE DISCOVERY SEA
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profile, the user defines his curre
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Abdelkafi, N. .....................
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