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..
Anomaly Ranking as Supervised Bipartite Ranking
Authors: Stephan Clémençon, Sylvain Robbiano
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments based on a variety of bipartite ranking algorithms well-documented in the literature are displayed in order to illustrate the relevance of our approach. |
| Researcher Affiliation | Academia | LTCI UMR Telecom Paris Tech/CNRS No. 5141, 46 rue Barrault, 75634 Paris Cedex, France CIMFAV-Facultad de Ingeniera, Universidad de Valparaso, Valparaso, Chile |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | Yes | Precisely, we implemented the modification of the TREERANK procedure based on locally weighted versions of the CART method (with axis parallel splits) described at length in subsection 4.2, using the package for R statistical software (see http://www.rproject.org), available at http://treerank.sourceforge.net (with parameters: minsplit = 1, maxdepth = 4, in the LEAFRANK), see (Baskiotis et al., 2009). |
| Open Datasets | Yes | We also used a benchmark dataset in anomaly detection (computer network intrusion detection namely) proposed as a challenge for intrusion detection in the CMDC2013, see http://www.csmining.org/cdmc2013/ and (Song, 2013). |
| Dataset Splits | Yes | In the following experiment, an estimate of the area under the MV-curve (AMV in short) is computed over 5 replications of a 5-fold cross validation as well as the overall standard deviation (denoted by σ). |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory, or cloud instance types) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions software like 'R statistical software', 'SVM-light implementation', 'Rank RLS method', and 'R-package Kernlab' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | Precisely, we implemented the modification of the TREERANK procedure... (with parameters: minsplit = 1, maxdepth = 4, in the LEAFRANK)... Rank Boost (aggregating 30 stumps)... SVMRank (with linear and Gaussian kernels with cross-validated parameters)... Rank RLS method... with linear kernel ( bias = 1 ) and with Gaussian kernel (γ = 0.01)... R-package Kernlab with gaussian kernel, with parameters chosen automatically by cross-validation. |