Messy Coding in the XCS Classifier System for Sequence Labeling
Messy Coding in the XCS Classifier System for Sequence Labeling
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DOI：
10.1007/978-3-319-10762-2_19
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发表时间：
2014-09
期刊：
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
XCS序列标记分类器系统（XCS-SL）是XCS序列标记的扩展，是一种时间序列分类的形式，其中每个输入都有一个类标签。在XCS-SL中，分类器条件由一些引用先前输入的子条件组成。每个子条件都是一个记忆。条件具有n个子条件，其表示从当前时间t0到前一时间tn的间隔。这种表示（称为间隔编码）的一个问题是，即使只需要一个输入att_nis，条件必须由n个子条件来引用它。我们引入了一个混乱的编码为基础的条件，其中每个子条件混乱地引用一个单一的前一个时间。与原始编码不同，子条件集不一定表示区间，因此它可以表示紧凑条件。原始的XCS-SL进化机制不能用于混乱的编码，我们的主要创新是一个新的进化机制。基准测试结果表明，与原始的区间编码相比，杂乱编码导致更小的种群规模，并且不需要那么高的种群规模限制。然而，混乱的编码需要更多的训练，具有高的种群大小限制。在一个真实的世界序列标记任务中，杂乱编码进化出了一种解决方案，该解决方案比原始区间编码具有更小的群体大小，从而实现了更高的准确性
The XCS classifier system for sequence labeling (XCS-SL) is an extension of XCS for sequence labeling, a form of time-series classification where every input has a class label. In XCS-SL a classifier condition consists of some sub-conditions which refer back to previous inputs. Each sub-condition is a memory. A condition has n sub-conditions which represent an interval from the current time t0 to a previous timet_n. A problem of this representation (called interval coding) is, even if only one input att_nis needed, the condition must consist of n subconditions to refer to it. We introduce a messy coding based condition where each sub-condition messily refers to a single previous time. Unlike the original coding, the set of sub-conditions does not necessarily represent an interval, so it can represent compact conditions. The original XCS-SL evolutionary mechanism cannot be used with messy coding and our main innovation is a novel evolutionary mechanism. Results on a benchmark show that, compared to the original interval coding, messy coding results in a smaller population size and does not require as high a population size limit. However, messy coding requires more training with a high population size limit. On a real world sequence labeling task messy coding evolved a solution that achieved higher accuracy with a smaller population size than the original interval coding