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David J. Lubinsky

dblp:54/6709 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 1995
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 1 first-authorArtificial intelligence and machine learning · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Kernel, tree and ensemble methods · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Kernel, tree and ensemble methods
decision tree		 0.011995
Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves · ICML 1995
Machine learning › Kernel, tree and ensemble methods › decision tree
decision tree classifiers		 0.011995
Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves · ICML 1995

Methods — techniques the papers use, named apart from their topics

accuracy criterion · 0.0
YearPublicationVenuePosition
1995 Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves
David J. Lubinsky
 ICML1
1991 Experimental evaluation of a test coverage analyzer for C and C + +
William B. Frakes, David J. Lubinsky, D. N. Neal
 J. Syst. Softw.2
1990 Measuring software size by distinct lines
abstract
The relationship between DLC (distinct line count) and NCSL (noncomment source lines) is studied on a number of programs, and it is found that, as a simple rule of thumb, the NCSL count can be estimated by twice the DLC. A more accurate model is derived by predicting NCSL from DLC and the number of lines that occur exactly once. It is also shown that, for unrelated programs, the proportion of common lines is very small; hence, DLC is approximately additive. It is concluded that, overall, the DLC is a very attractive measure of size that has two basic advantages over NCSL: it is an intuitively more appealing measure of effort than NCSL, and the problems of measuring size of subsequent releases disappear when using DLC.>
David J. Lubinsky
 COMPSAC1