Department of Computer Science, School of Electrical and Computer Engineering, National Defense Academy of Japan, Yokosuka, 239-8686, Japan.
Sci Rep. 2018 May 9;8(1):7397. doi: 10.1038/s41598-018-25679-z.
Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.
人类学习者可以从少量样本中概括出新的概念。相比之下，传统的机器学习方法需要大量的数据来解决相同类型的问题。人类有认知偏见，这促进了快速学习。在这里，我们开发了一种方法，通过利用认知偏见来缩小人类和机器在这种推理类型上的差距。我们将人类认知模型实现到机器学习算法中，并将它们的性能与目前最流行的方法，如朴素贝叶斯、支持向量机、神经网络、逻辑回归和随机森林进行了比较。我们专注于垃圾邮件分类任务，这在机器学习领域已经研究了很长时间，通常需要大量的数据才能获得高精度。与其他有代表性的机器学习方法相比，我们的模型在小样本和有偏差的样本上表现出了优越的性能。
