Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Precision-based Boosting
Authors: Mohammad Hossein Nikravan, Marjan Movahedan, Sandra Zilles9153-9160
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An empirical study on Ada Boost and one of its multi-class versions, SAMME, demonstrates the superiority of our method on datasets with more than 1,000 instances as well as on datasets with more than three classes. |
| Researcher Affiliation | Academia | Mohammad Hossein Nikravan, Marjan Movahedan, Sandra Zilles Department of Computer Science, University of Regina, Regina, SK, Canada EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Ada Boost Scheme (Freund and Schapire 1997), Algorithm 2: Pr Ada Boost |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We evaluated (Pr)Ada Boost on 23 binary UCI datasets (Lichman 2013) and (Pr)SAMME on 18 multi-class UCI datasets |
| Dataset Splits | Yes | We performed 10-fold cross validation on each dataset (except isolet , which has designated training and test portions), comparing two algorithms always on the same folds. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions "decision stumps trained in Matlab as base classifiers" but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | We ran Pr Ada Boost and Ada Boost for T iterations, trying T = 30, 50, and 100 (without attempting to tune T.)... using decision stumps trained in Matlab as base classifiers. |