Ethological data mining: an automata-based approach to extract behavioral units and rules
Ethological data mining: an automata-based approach to extract behavioral units and rules
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DOI：
10.1007/s10618-008-0122-1
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发表时间：
2009-06-01
影响因子：
4.8
通讯作者：
Okanoya, Kazuo
中科院分区：
文献类型：
--
作者：
Kakishita, Yasuki;Sasahara, Kazutoshi;Okanoya, Kazuo
我们提出了一种有效的基于自动机的方法，从动物行为的连续序列数据中提取行为单元和规则。通过引入新颖的扩展，我们将两种基本方法（N-gram 模型和 Angluin 机器学习算法）集成到行为学数据挖掘框架中。这使我们能够获得行为规则的最小化自动机表示，这些规则从动物行为的连续数据中接受（或生成）最小的可能行为模式集。通过这种方法，我们演示了如何使用真实的鸟鸣数据进行行为学数据挖掘；我们使用孟加拉雀的鸣声，并使用计算机程序生成的人工鸟鸣数据对该方法进行实验评估。这些结果表明，通过适当设置我们引入的参数，即使对于嘈杂的行为数据，我们的行为学数据挖掘也能有效地发挥作用。此外，我们还用孟加拉雀的歌曲进行了案例研究，表明我们的方法成功地掌握了歌唱行为的核心结构，例如循环和分支。
We propose an efficient automata-based approach to extract behavioral units and rules from continuous sequential data of animal behavior. By introducing novel extensions, we integrate two elemental methods-the N-gram model and Angluin's machine learning algorithm into an ethological data mining framework. This allows us to obtain the minimized automaton-representation of behavioral rules that accept (or generate) the smallest set of possible behavioral patterns from sequential data of animal behavior. With this method, we demonstrate how the ethological data mining works using real birdsong data; we use the Bengalese finch song and perform experimental evaluations of this method using artificial birdsong data generated by a computer program. These results suggest that our ethological data mining works effectively even for noisy behavioral data by appropriately setting the parameters that we introduce. In addition, we demonstrate a case study using the Bengalese finch song, showing that our method successfully grasps the core structure of the singing behavior such as loops and branches.