Solving M-Modes Using Heuristic Search

Authors: Cong Chen, Changhe Yuan, Chao Chen

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We evaluated our method by comparing to the algorithm by Chen et al. [2014] (called DP) on both synthetic and real datasets.
Researcher Affiliation Academia Cong Chen, Changhe Yuan, Chao Chen CUNY Graduate Center and CUNY Queens College
Pseudocode Yes Algorithm 1 provides a pseudo code for this preprocessing step.
Open Source Code No The paper does not provide any statement or link indicating the availability of open-source code for the described methodology.
Open Datasets Yes We selected data of different dimensions from UCI repository [Lichman, 2013]. We also used the ADHD-200 dataset [Biswal et al., 2010].
Dataset Splits No The paper does not provide specific details on training, validation, or test dataset splits or cross-validation setup.
Hardware Specification No The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments.
Software Dependencies No The paper does not provide specific software dependencies with version numbers (e.g., library names, programming languages, or solvers with their versions) that would be needed for replication.
Experiment Setup Yes For each setting, we generated ten tree models and computed the average running time. The label size L was fixed to be three. This experiment is designed to systematically test the influence of each parameter on the search and DP algorithms. The base setting is d = 60, t = 6, δ = 3, and M = 4.