An Empirical Study of Knowledge Tradeoffs in Case-Based Reasoning
Authors: Devi Ganesan, Sutanu Chakraborti
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 {gdevi, sutanuc}@cse.iitm.ac.in |
| 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. |