论文标题
使用Neurochaos学习的原因效应保存和分类
Cause-Effect Preservation and Classification using Neurochaos Learning
论文作者
论文摘要
从观察数据中发现原因效应是科学和工程学中的一个重要但具有挑战性的问题。在这项工作中，最近提出的大脑启发的学习算法，即 - \ emph {neurochaos Learning}（NL），用于从模拟数据中分类原因。使用的数据实例是从耦合的AR过程，耦合的1D混沌偏斜帐篷映射，耦合的1D混沌Logistic Maps和现实世界中的Prey-Predator系统中生成的。提出的方法始终优于五层深神经网络体系结构，用于耦合系数值的范围从$ 0.1 $到$ 0.7 $。此外，我们研究了使用Granger因果关系（GC）在耦合AR过程中提取的特征提取空间中的因果关系，以及用于耦合混乱系统和现实世界中的猎物predater数据集的耦合AR过程和压缩 - 复杂性因果关系（CCC）。 NL在混乱的转变下保留因果关系并成功地对因果时间序列（包括转移学习方案）进行分类的能力是很高的。
Discovering cause-effect from observational data is an important but challenging problem in science and engineering. In this work, a recently proposed brain inspired learning algorithm namely-\emph{Neurochaos Learning} (NL) is used for the classification of cause-effect from simulated data. The data instances used are generated from coupled AR processes, coupled 1D chaotic skew tent maps, coupled 1D chaotic logistic maps and a real-world prey-predator system. The proposed method consistently outperforms a five layer Deep Neural Network architecture for coupling coefficient values ranging from $0.1$ to $0.7$. Further, we investigate the preservation of causality in the feature extracted space of NL using Granger Causality (GC) for coupled AR processes and and Compression-Complexity Causality (CCC) for coupled chaotic systems and real-world prey-predator dataset. This ability of NL to preserve causality under a chaotic transformation and successfully classify cause and effect time series (including a transfer learning scenario) is highly desirable in causal machine learning applications.
