A modified XCS classifier system for sequence labeling
A modified XCS classifier system for sequence labeling
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DOI：
10.1145/2576768.2598352
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发表时间：
2014-07
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影响因子：
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通讯作者：
Masaya Nakata;T. Kovacs;K. Takadama
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文献类型：
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作者：
Masaya Nakata;T. Kovacs;K. Takadama
本文介绍了XCS-SL，它是XCS的一种扩展，用于序列标注，它是一种时间序列分类形式，其中每一个输入都有一个类别标签。具体地说，我们考虑序列标记任务，其中在每个时间步上我们接收一个输入/类对。按顺序标记输入的正确类别可能取决于在先前时间戳上接收的数据，因此学习者可能需要参考先前时间戳上的数据。也就是说，一些分类规则(这里称为分类器)必须包括关于先前输入的条件(一种记忆)。我们假设代理不知道需要关于先前输入的多少个条件来对当前输入进行分类，并且对于每个输入所需的条件/记忆的数量可能不同。因此，使用固定数量的条件不是一个好的解决方案。我们引入的一个新想法是分类器，它具有可变长度的条件来引用以前的数据。条件可以增大和缩小，以找到合适的内存大小。在基准问题上，XCS-SL可以学习最优分类器，在现实世界的序列标注任务中，它获得了高分类精度，并发现了有趣的知识，表明了不同时间输入之间的依赖关系。
This paper introduces XCS-SL, an extension of XCS for sequence labeling, a form of time-series classification where every input has a class label. Specifically, we consider sequence labeling tasks where on each time step we receive an input/class pair. In sequence labeling the correct class of an input may depend on data received on previous time stamps, so a learner may need to refer to data at previous time stamps. That is, some classification rules (called classifiers' here) must include conditions on previous inputs (a kind of memory). We assume the agent does not know how many conditions on previous inputs are needed to classify the current input, and the number of conditions/memories needed may be different for each input. Hence, using a fixed number of conditions is not a good solution. A novel idea we introduce is classifiers that have a variable-length condition to refer back to data at previous times. The condition can grow and shrink to find a suitable memory size. On a benchmark problem XCS-SL can learn optimal classifiers, and on a real-world sequence labeling task, it derived high classification accuracy and discovered interesting knowledge that shows dependencies between inputs at different times.