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..
MDP-Based Cost Sensitive Classification Using Decision Trees
Authors: Shlomi Maliah, Guy Shani
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide experiments over a set of benchmarks (Lomax and Vadera 2013) showing that our MDP approach scales well over these benchmarks. |
| Researcher Affiliation | Academia | Software and Information Systems Engineering Ben Gurion University, Israel EMAIL, EMAIL |
| Pseudocode | No | The paper describes the algorithms and their equations (1-5, 7-12) in narrative text and within equations, but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper mentions implementation languages for various methods, including their own in C#, but does not provide concrete access to its source code or state that it is open-source. |
| Open Datasets | Yes | We experiments with all domains from the recent cost sensitive survey (Lomax and Vadera 2013; 2011)... Dataset statistics are reported in Table 5. |
| Dataset Splits | Yes | We use a standard 80% 20% train-test split. |
| Hardware Specification | Yes | The experiments were run on a Windows 10 machine, i5 CPU, and 8GB RAM. |
| Software Dependencies | No | The paper mentions software like Weka and programming languages (C#, C, Java) but does not provide specific version numbers for these or any dependent libraries. |
| Experiment Setup | Yes | We use a standard 80% 20% train-test split. For the larger datasets Heart, Wine and Hepatitis, we learned decision trees only over subsets of the 10 most costly attributes together with the low cost attributes. |