Machine learning has become a powerful tool in real applications such as decision making, sentiment prediction and ontology engineering. In the form of learning strategies, machine learning can be specialized into two types: supervised learning and unsupervised learning. Classification is a special type of supervised learning task, which can also be referred to as categorical prediction. In other words, classification tasks involve predictions of the values of discrete attributes. Some popular classification algorithms include Naïve Bayes and K Nearest Neighbour. The above type of classification algorithms generally involves voting towards classifying unseen instances. In traditional ways, the voting is made on the basis of any employed statistical heuristics such as probability. In Naïve Bayes, the voting is made through selecting the class with the highest posterior probability on the basis of the values of all independent attributes. In K Nearest Neighbour, majority voting is usually used towards classifying test instances. This kind of voting is considered to be biased, which may lead to overfitting. In order to avoid such overfitting, this paper proposes to employ a nature and biology inspired approach of voting referred to as probabilistic voting towards reduction of bias. An extended experimental study is reported to show how the probabilistic voting can manage to effectively reduce the bias towards improvement of classification accuracy.
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