Causal cognition and spam classifier

Abstract

Previous studies have shown that some combo of human cognitive biases is effective in machine learning. The well-used model of the biases is called loosely symmetric (LS) model. We show the efficiency and accuracy of our loosely symmetric model and its implementation of two cognitive biases, symmetry and mutual exclusively.In this study, we use loosely symmetric as a binary classifier to enhance its accuracy in small datasets.

Implementation of Human Cognitive Bias on Naïve Bayes

“…In future work, we will elucidate the reason why our model did not improve the prediction accuracy in ℎ classification and detect the composition difference between and ℎ documents. Also, we will try to minimize the number of sample data since LSNB and eLSNB are supposed to be learned effectively from a small number of sample data as compared to NB classifier [9] and measure execution time and resource consumption. In order to improve the prediction accuracy of both categories, we will modify and improve our models to adapt to any training corpus.…”

“…Previous researches [6,7,8,9] have shown the capability of implementing human-cognition inspired model for machinelearning tasks. The well-used model called LS model flexibly adjusts the two biases ( and ) and has correlation to human-cognition [7].…”

“…The well-used model called LS model flexibly adjusts the two biases ( and ) and has correlation to human-cognition [7]. The LS model shows the superior performance on machine learning tasks including twoarmed bandit problems [8] and Naïve Bayes [9]. It is known that human has illogical symmetric cognitive biases that induces from a proposition "if then " its converse "if then " and inverse "if ̅ then not ̅".…”

“…Oyo et.al. [8] reported that LS model is able to describe the human evaluation of co-occurrence information for inductive inference of causal relationships, and developed an excellent heuristics for evaluating options for two-armed bandit problems and Naïve Bayes [9]. Furthermore, we thus assume that the LS model can smoothly adjust biases between classes and factors more than the conventional Naïve Bayes model.…”

“…Meanwhile, some studies [6,7,8,9] have indicated that the HumanCognitively inspired bias is able to enhance the prediction accuracy of machine learning algorithms. This HumanCognitively inspired model called "Loosely Symmetric (LS) model" introduced by Shinohara et.al.…”