Anomaly Ranking as Supervised Bipartite Ranking
Authors: Stephan Clémençon, Sylvain Robbiano
ICML 2014 | Conference PDF | Archive PDF | Plain Text | 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. |