A Symbolic Approach to Explaining Bayesian Network Classifiers
Authors: Andy Shih, Arthur Choi, Adnan Darwiche
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We consider in particular the compilation of Naive and Latent-Tree Bayesian network classifiers into Ordered Decision Diagrams (ODDs), providing a context for evaluating our proposal using case studies and experiments based on classifiers from the literature. |
| Researcher Affiliation | Academia | Andy Shih and Arthur Choi and Adnan Darwiche Computer Science Department, University of California, Los Angeles {andyshih,aychoi,darwiche}@cs.ucla.edu |
| Pseudocode | Yes | Algorithm 1 compile-naive-bayes(N), Algorithm 2 compile-latent-tree(N), Algorithm 3 find-mc-explanation(f(X), x), Algorithm 4 pi-cover(f, π), Algorithm 5 pi-inst(f, π, x) are all presented as clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We now consider the Congressional Voting Records (votes) from the UCI machine learning repository [Bache and Lichman, 2013]. |
| Dataset Splits | No | The paper mentions using a dataset and training a classifier, but it does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or k-fold cross-validation setup). |
| Hardware Specification | No | The paper does not mention any specific hardware used for running the experiments (e.g., GPU models, CPU types, or cloud computing instances with specifications). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | To completely specify the naive Bayes classifier, we also need the prior probability of admission, which we assume to be Pr(A=+) = 0.30. Moreover, we use a decision threshold of 0.50, admitting an applicant x if Pr(A=+ | x) .50. |