## HINT

swMATH ID: | 27284 |

Software Authors: | Zupan, Blaž; Bohanec, Marko; Demšar, Janez; Bratko, Ivan |

Description: | Learning by discovering concept hierarchies. We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and their definitions. This effectively decomposes the problem into smaller, less complex problems. The method is inspired by the Boolean function decomposition approach to the design of switching circuits. To cope with high time complexity of finding an optimal decomposition, we propose a suboptimal heuristic algorithm. The method, implemented in program HINT (Hierarchy INduction Tool), is experimentally evaluated using a set of artificial and real-world learning problems. In particular, the evaluation addresses the generalization property of decomposition and its capability to discover meaningful hierarchies. The experiments show that HINT performs well in both respects. |

Homepage: | https://www.sciencedirect.com/science/article/pii/S0004370299000089 |

Keywords: | function decomposition; machine learning; concept hierarchies; concept discovery; constructive induction; generalization |

Related Software: | DEX; C4.5; UCI-ml |

Cited in: | 3 Publications |

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### Cited by 8 Authors

1 | Bioch, Jan C. |

1 | Bohanec, Marko |

1 | Bratko, Ivan |

1 | Demšar, Janez |

1 | Li, Deyu |

1 | Qu, Kaishe |

1 | Zhai, Yanhui |

1 | Zupan, Blaž |

### Cited in 3 Serials

1 | Artificial Intelligence |

1 | Discrete Applied Mathematics |

1 | Journal of Computer and System Sciences |

### Cited in 3 Fields

3 | Computer science (68-XX) |

1 | Order, lattices, ordered algebraic structures (06-XX) |

1 | Information and communication theory, circuits (94-XX) |