A machine learning model with human cognitive biases capable of learning from small and biased datasets

Abstract

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.

Approximate and Situated Causality in Deep Learning

“…For example, machine learning models with human cognitive biases are already capable of learning from small and biased datasets [44]. This process reminds me of the role of the Student test in relation to frequentist ideas, always requesting large sets of data until the creation of the t-test, something that could be applied now in the context of machine learning.…”

“…In [44], the authors developed a method to reduce the inferential gap between human beings and machines by utilizing cognitive biases. They 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.…”

Approximate and Situated Causality in Deep Learning

“…For example, machine learning models with human cognitive biases are already capable of learning from small and biased datasets [63]. This process reminds the role of Student test in relation to frequentist ideas, always requesting large sets of data until the creation of the t-test, but now in the context of machine learning.…”

“…In [63] boost the potentiality of DL, diminishing the computational power of the systems as well as adding new heuristic approaches to information analysis [35], [65]- [67].…”

Implementation of Human Cognitive Bias on Neural Network and Its Application to Breast Cancer Diagnosis

“…The purposes of our model are to improve classification accuracy with a small number of biased training examples and to create a new NN framework which utilizes human cognitive biases. Previous studies such as [17], [30], [31] showed the effectiveness of human cognitive biases for machine learning tasks, especially when the small and biased number of examples were given to the model. In our model, NN with LS can omit nodes and "revive" them according to the status of the network.…”