reproducibilityindex.ai

MDP-Based Cost Sensitive Classification Using Decision Trees

Authors: Shlomi Maliah, Guy Shani

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.