Divya Bhadoria

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1ranked-venue papers

0as first author

0since 2021 · last 2003

—ORCID · none

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

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1

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.

Databases, data mining, and information retrieval

1 paper

Data mining · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Data mining › predictive modeling › classification › ensemble learning bagging	1	2003	Comparing Pure Parallel Ensemble Creation Techniques Against Bagging · ICDM 2003
Data mining › predictive modeling › classification ensemble learning	1	2003	Comparing Pure Parallel Ensemble Creation Techniques Against Bagging · ICDM 2003
Data mining › predictive modeling classification	1	2003	Comparing Pure Parallel Ensemble Creation Techniques Against Bagging · ICDM 2003
Data mining › predictive modeling › classification decision tree learning	1	2003	Comparing Pure Parallel Ensemble Creation Techniques Against Bagging · ICDM 2003

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

randomization-based ensemble creation · 0.0

Year	Publication	Venue	Position
2003	Comparing Pure Parallel Ensemble Creation Techniques Against Bagging abstract We experimentally evaluate randomization-based approaches to creating an ensemble of decision-tree classifiers. Unlike methods related to boosting, all of the eight approaches considered here create each classifier in an ensemble independently of the other classifiers. Experiments were performed on 28 publicly available datasets, using C4.5 release 8 as the base classifier. While each of the other seven approaches has some strengths, we find that none of them is consistently more accurate than standard bagging when tested for statistical significance. Lawrence O. Hall, Kevin W. Bowyer, Robert E. Banfield, Divya Bhadoria, W. Philip Kegelmeyer, Steven Eschrich	ICDM	4