Decision Boundary for Discrete Bayesian Network Classifiers
Authors: Gherardo Varando, Concha Bielza, Pedro Larrañaga
JMLR 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we try to generalize the above results within a unified framework. To do this we compute polynomial threshold functions for Bayesian network (BN) binary classifiers in order to express their decision boundaries. This research is restricted to BN classifiers where the binary class variable, C, has no parents and where the predictors are categorical. As usual, our results extend to non-binary classifiers considering an ensemble of binary classifiers. Polynomial threshold functions are a way to describe the decision boundary of a discrete classifier and are a generalization of the results of Minsky (1961) and Peot (1996). |
| Researcher Affiliation | Academia | Gherardo Varando EMAIL Concha Bielza EMAIL Pedro Larra naga EMAIL Departamento de Inteligencia Artificial Universidad Polit ecnica de Madrid Campus de Montegancedo, s/n 28660 Boadilla del Monte, Madrid, Spain |
| Pseudocode | No | The paper describes methods and proofs using mathematical notation and prose, but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | No | The paper does not contain any explicit statements about making source code available, nor does it provide links to any code repositories or supplementary materials containing code. |
| Open Datasets | No | The paper uses illustrative examples with categorical variables and probability tables (e.g., Table 1, Table 3), but these are constructed for theoretical demonstration rather than being publicly available datasets used for empirical evaluation. There is no mention of specific public datasets or access information for any data. |
| Dataset Splits | No | The paper focuses on theoretical derivations and proofs related to decision boundaries for Bayesian network classifiers, using abstract or illustrative examples. It does not describe any empirical experiments that would require dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments or implementations. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical, presenting mathematical derivations and proofs. It does not mention any specific software, libraries, or programming languages with version numbers that would be necessary to reproduce experimental results. |
| Experiment Setup | No | The paper focuses on theoretical analysis, providing mathematical derivations and proofs for Bayesian network classifiers. It does not describe any empirical experiments, and therefore, no experimental setup details such as hyperparameters, training configurations, or system-level settings are provided. |