An Improved Bayesian Algorithm Based on Contribution Rate of Attribute Value
Zhongmei Zhou · Journal of Zhangzhou Normal University · 2010
The Naive Bayesian is a simple and efficient way of classification.When the assumption of attribute independence does not hold,it possibly leads to misjudgment in types of the will-be-tested samples.When the will-be-tested samples have the same probabilities in all categories,it is unable to judge the type of samples.Those affect the accuracy in data's classification.An improved algorithm of Bayesian based on contribution rate of attribute value is proposed in the paper,that is,the type of samples will be judged by the total contribution rate of all attribute value of will-be-tested samples in all categories.The result of mushroom data experiments show that the improved algorithm can effectively improve the accuracy of data classification.