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..
An Empirical Study of Knowledge Tradeoffs in Case-Based Reasoning
Authors: Devi Ganesan, Sutanu Chakraborti
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed measure is empirically evaluated on synthetic as well as real-world datasets. From a practical standpoint, footprint size reduction provides a unified way of estimating the impact of a given piece of knowledge in any knowledge container, and can also suggest ways of characterizing the nature of domains ranging from ill-defined to well-defined ones. |
| Researcher Affiliation | Academia | Devi Ganesan and Sutanu Chakraborti Indian Institute of Technology Madras, Chennai, India EMAIL |
| Pseudocode | No | The paper describes algorithms and functions (e.g., 'solves function', 'footprint algorithm') but does not provide them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any links to open-source code for the described methodology or state that it will be released. |
| Open Datasets | Yes | We generated a synthetic case base (Table 1)... Next, we discuss empirical results on three real world datasets taken from UCI machine learning repository [Dheeru and Karra Taniskidou, 2017] namely Iris, Auto-MPG and Boston Housing and two textual datasets based on 20 Newsgroups [Lang, 1999]. |
| Dataset Splits | Yes | In all the experiments on synthetic case base, the results are averaged from 5 fold train-test splits, and the relation between footprint size reduction and knowledge transfers is tested for statistical significance. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, cloud instances, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions techniques like TFIDF and LSA but does not list any specific software libraries or their version numbers used for the experiments. |
| Experiment Setup | No | The paper mentions 'acceptable prediction error' values (e.g., 10%) but does not provide specific hyperparameters (like learning rate, batch size, epochs, optimizer settings) or other detailed system-level training configurations to reproduce the experiments. |