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Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries

机译：在复杂数据中发现简单规则：元学习算法和一些令人惊讶的音乐发现

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摘要

This article presents a new rule discovery algorithm named PLCG that can find simple, robust partial rule models (sets of classification rules) in complex data where it is difficult or impossible to find models that completely account for all the phenomena of interest. Technically speaking, PLCG is an ensemble learning method that learns multiple models via some standard rule learning algorithm, and then combines these into one final rule set via clustering, generalization, and heuristic rule selection. The algorithm was developed in the context of an interdisciplinary research project that aims at discovering fundamental principles of expressive music performance from large amounts of complex real-world data (specifically, measurements of actual performances by concert pianists). It will be shown that PLCG succeeds in finding some surprisingly simple and robust performance principles, some of which represent truly novel and musically meaningful discoveries. A set of more systematic experiments shows that PLCG usually discovers significantly simpler theories than more direct approaches to rule learning (including the state-of-the-art learning algorithm RIPPER), while striking a compromise between coverage and precision. The experiments also show how easy it is to use PLCG as a meta-learning strategy to explore different parts of the space of rule models.
机译：本文介绍了一种名为PLCG的新规则发现算法，该算法可以在很难或不可能找到完全解决所有关注现象的模型的复杂数据中找到简单，健壮的局部规则模型（分类规则集）。从技术上讲，PLCG是一种整体学习方法，它通过某种标准规则学习算法来学习多个模型，然后通过聚类，泛化和启发式规则选择将它们组合成一个最终规则集。该算法是在跨学科研究项目的背景下开发的，该项目旨在从大量复杂的真实世界数据（特别是音乐会钢琴演奏家对实际演奏的测量）中发现表现音乐演奏的基本原理。将显示PLCG成功地找到了一些令人惊讶的简单而强大的性能原理，其中一些代表了真正新颖和音乐上有意义的发现。一组更系统的实验表明，与更直接的规则学习方法（包括最新的学习算法RIPPER）相比，PLCG通常会发现简单得多的理论，同时还要在覆盖率和精度之间做出折衷。实验还表明，使用PLCG作为元学习策略来探索规则模型空间的不同部分是多么容易。

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