Precision-based Boosting
Authors: Mohammad Hossein Nikravan, Marjan Movahedan, Sandra Zilles9153-9160
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 nikravam@uregina.ca, marjan.movahedan@gmail.com, zilles@cs.uregina.ca |
| 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. |