Fast IAMB (bnlearn)

Module name	bnlearn_fastiamb
Package	bnlearn
Version	4.8.3
Language	R
Docs	here
Paper	Yaramakala and Margaritis[1]
Graph type	DAG
MCMC	No
Edge constraints	Yes
Data type	C, D, M
Data missingness
Intervention type
Docker	bpimages/bnlearn:4.8.3

Fast IAMB

Abstract: In this paper we address the problem of learning the Markov blanket of a quantity from data in an efficient manner Markov blanket discovery can be used in the feature selection problem to find an optimal set of features for classification tasks, and is a frequently-used preprocessing phase in data mining, especially for high-dimensional domains. Our contribution is a novel algorithm for the induction of Markov blankets from data, called Fast-IAMB, that employs a heuristic to quickly recover the Markov blanket. Empirical results show that Fast-IAMB performs in many cases faster and more reliably than existing algorithms without adversely affecting the accuracy of the recovered Markov blankets.

Some fields described

edgeConstraints Name of the JSON file containing background knowledge

Example JSON

[
  {
    "id": "fastiamb-zf",
    "alpha": [
      0.01,
      0.05
    ],
    "test": "zf",
    "B": null,
    "maxsx": null,
    "debug": false,
    "undirected": false,
    "timeout": null,
    "edgeConstraints": "edgeConstraints.json"
  },
  {
    "id": "fastiamb-mi",
    "alpha": [
      0.01,
      0.05,
      0.1,
      0.2
    ],
    "test": "mi",
    "B": null,
    "maxsx": null,
    "debug": false,
    "undirected": false,
    "timeout": null,
    "edgeConstraints": "edgeConstraints.json"
  }
]