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..
Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME
Authors: Farhad Shakerin, Gopal Gupta3052-3059
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics. |
| Researcher Affiliation | Academia | Farhad Shakerin, Gopal Gupta Computer Science Department, The University of Texas at Dallas, Richardson, USA {farhad.shakerin,gupta}utdallas.edu |
| Pseudocode | Yes | Algorithm 1 Summarizing the FOIL algorithm; Algorithm 2 Linear Model Generation by LIME; Algorithm 3 FOLD Algorithm; Algorithm 4 Dataset Transformation with LIME |
| Open Source Code | No | The paper mentions that 'ALEPH v.5 has been ported into SWI-Prolog by (Riguzzi 2016)' with a GitHub link, but this is for a third-party tool (ALEPH) and not for the authors' own LIME-FOLD methodology. There is no concrete access provided for the LIME-FOLD source code. |
| Open Datasets | Yes | In this section, we present our experiments on UCI standard benchmarks (Lichman 2013). The ALEPH system (Srinivasan 2001) is used as the baseline. Lichman, M. 2013. UCI,ml repository, http://archive.ics. uci.edu/ml. |
| Dataset Splits | Yes | First, we run ALEPH on 10 different datasets using 5-fold crossvalidation setting. Second, each dataset is transformed as explained in Algorithm 4. Then the LIME-FOLD algorithm is run on a 5-fold cross-validated setting, and the classification metrics are reported. |
| Hardware Specification | Yes | All experiments were run on an Intel Core i7 CPU @ 2.7GHz with 16 GB RAM and a 64-bit Windows 10. |
| Software Dependencies | Yes | The FOLD algorithm is a Java application that uses JPL library to connect to SWI prolog. ALEPH v.5 has been ported into SWI-Prolog by (Riguzzi 2016). |
| Experiment Setup | Yes | We set ALEPH to use the heuristic enumeration strategy, and the maximum number of branch nodes to be explored in a branch-and-bound search to 500K. In this research we conducted all experiments using the Extreme Gradient Boosting (XGBoost) algorithm (Chen and Guestrin 2016). |