A Novel Approach for Enhancing Success Rate in Social Media Profile Matching using Decision Table over Random committee

Abstract

To Predict the Success rate enhancement in social media profile matching. table with sample size 10 and random committee sample size 10. User profiles are matched based on their location and date of birth. The sigmoid function used in decision table prediction to probability which helps to improve the prediction of accuracy. The decision table algorithm has a slight increase in significant value of p=0.00(p \gt 0.50). G-power calculations are used to generate the necessary sample for this investigation. The analysis’s minimum power is set at 0.8, while the maximum allowed error is set at 0.5 percent. In the proposed system, the decision table is used for profile matching. Random committee algorithm is used to compare the results of decision table. The profile matching and comparisons are done based on the location and date of birth. Random committee algorithm is showing is less accuracy (76.9%) than decision table (85%). Predicting social media profile matching significantly better novel decision table than random committee.