MARC details
| 000 -LEADER |
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| fixed length control field |
02088nam a22003373a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
UPMIN-00003211653 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
UPMIN |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20221123131047.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
221123b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
DLC |
| 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 2008 |
| Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) |
A64 A66 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Aporbo, Jenelyn Calderon. |
| 245 #2 - TITLE STATEMENT |
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| Title |
A K-nearest neighbor imputation method based on locally weighted scatterplot smoothing (LOESS) for ordinal data |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Date of publication, distribution, etc. |
2008 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Date of publication, distribution, etc. |
2008 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
54 leaves. |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2008 |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
This study introduced a modification of the classical k-nearest neighbor algorithm called Weighted KNN based on Locally Weighted Seatterplot Smoothing (LOESS). It uses weighting scheme to make sure that the degree of influence of each neighbor is accounted in estimating missing values. The newly developed imputation technique was evaluated and compared to the existing methods using Dermatology and Breast Cancer data sets. The incomplete data sets in the experiment were generated under MCAR condition only with 1%, 5%, 10% and 15% levels of missing values. K-means and k-modes clustering algorithms were used to determine the recovery of each technique. Results showed that Weighted KNN based on LOESS performed best when applied on both Dermatology and Breast Cancer data sets with K-means clustering algorithm. It ranked next to KNN when applied on Dermatology data set with k-modes clustering algorithm and other outperformed the rest of the techniques on Breast Cancer data set. In general, Weighted KNN based on LOESS showed promising results when tested on both data sets. |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Clustering. |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Imputation. |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
K-means. |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Algorithms. |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
K-nearest neighbor algorithm. |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
LOESS (Locally Weighted Scatterplot Smoothing) |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
KNN (K-nearest neighbor) |
| 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) |
|---|
| a |
UP |
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
|---|
| a |
Fi |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |
