Outlier detection using weighted holoentropy with hyperbolic tangent function

Numerous research works has been carried out in the literature to detect the outlier's a.k.a anomalies. Outlier detection is considered as a pre-processing step for locating those objects in a dataset that do not conform to well-defined notions of expected behaviour. In the proposed method, logistic sigmoid function related to hyperbolic tangent will be used as weightage function for finding the outlier data point(s). It can distribute the outlier data points effectively as compared with the reverse sigmoid function. The method is implemented with four phases. In the first phase, data is read out and dynamic entropy is calculated. In the second phase, probability and dynamic entropy computations using logistic sigmoid function related to hyperbolic tangent are performed. In the third phase, dynamic entropies are sorted and top N point is selected as outlier data point(s) and finally, the accuracy for correct outliers is computed for the proposed method.