MARC details
| 000 -LEADER |
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| fixed length control field |
02879nam a22003373a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
UPMIN-00003300450 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
UPMIN |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20230213111706.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230213b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
D:C |
| Transcribing agency |
UPMin |
| Modifying agency |
upmin |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
eng |
| 090 #0 - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
LG993.5 2009 |
| Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) |
A64 V36 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Valencia, Jennyfer Ann Valencia. |
| 9 (RLIN) |
2406 |
| 245 ## - TITLE STATEMENT |
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| Title |
Evaluation of the performance of particles swarm optimization in parameter estimation for logistic neighbor in loan classification / |
| Statement of responsibility, etc. |
Jennyfer Ann Valencia Valencia. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Date of publication, distribution, etc. |
2009 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
82 leaves. |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2009 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Credit scoring is a classification problem wherein loan applicants are classified in good or bad depending on their characteristics. Mounting losses from the delinquent loans have urged researchers to improve the accuracy of the loan classifiers. One way of improving the accuracy is through the tuning of parameters of the classifiers such that the number of accepted applicants that will violate the credit agreement is minimized. Thus, credit scoring is an optimization problem as well. One of the most effective optimization tools is particle swarm optimization (PSO). hence, PSO was used in the study to find the weights in the commonly used credit scoring classifiers, logistic regression and distance weighted k-nearest neighbors (DWKNN). The effectiveness of PSO in finding these weights was compared to the commonly used tools in weight estimation, maximum likelihood estimation for logistic regression (MLE-LR) and the inverse of the computed distances for DWKNN (ID-DWKNN). Four classifiers were developed: PSO-LR, PSO-DWKNN, MLE-LR and ID-DWKNN. It was found that the accuracy of PSO-DWKNN in detecting the applicants that will default was better than ID-DWKNN when the training set was composed of applicants that were picked randomly. The classifiers MLE-LR and PSO-LR were found to perform better than the two classifiers when the training set had equal number of applicants from both classes. Both were able to classify all the bad applicants correctly. Nevertheless, it is noteworthy that PSO was not able to find the optimal set of weights for logistic regression and DWKNN. PSO, however should not be given up entirely as its performance is also dependent on the training set and the nature of the data. |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Distance weighted k-nearest neighbor. |
| 9 (RLIN) |
2407 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Credit scoring. |
| 9 (RLIN) |
2408 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Logistic regression. |
| 9 (RLIN) |
2409 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Maximum likelihood estimation. |
| 9 (RLIN) |
2410 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Particle swarm optimization. |
| 9 (RLIN) |
2333 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Loans. |
| 9 (RLIN) |
2386 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Evolutionary algorithms. |
| 9 (RLIN) |
1378 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Genetic algorithms. |
| 9 (RLIN) |
1379 |
| 658 ## - INDEX TERM--CURRICULUM OBJECTIVE |
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| Main curriculum objective |
Undergraduate Thesis |
| Curriculum code |
AMAT200, |
| Source of term or code |
BSAM |
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
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| a |
Fi |
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
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| a |
UP |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |
