Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Classification with Minimax Distance Measures
Authors: Morteza Haghir Chehreghani
AAAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,582 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,889 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally study our framework on different synthetic and real-world datasets and illustrate its effectiveness and superior performance in different settings. |
| Researcher Affiliation | Industry | Morteza Haghir Chehreghani Xerox Research Centre Europe XRCE 6 chemin de Maupertuis 38240 Meylan, France EMAIL |
| Pseudocode | No | The paper describes algorithms and procedures in prose but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository links or explicit statements of code release) for the source code of the described methodology. |
| Open Datasets | Yes | We perform our real-world experiments on twelve datasets from different domains, selected from the UCI repository (Lichman 2013). |
| Dataset Splits | No | The paper mentions that '60% of the objects are used for training' but does not specify a separate validation split or explicit cross-validation details for hyperparameter tuning. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Logistic Regression and SVM but does not provide specific version numbers for these software libraries or any other dependencies. |
| Experiment Setup | Yes | With SVM, we examine three different kernels: i. linear (lin), ii. radial basis function (rbf), and iii. sigmoid (sig), and choose the best result. With Minimax distances, we only use the linear kernel, since we assume that Minimax distances must be able to capture the correct classes, such that they can be then discriminated via a linear separator. ... We have repeated the random split of the data for 20 times and report the average results. |