Novel algorithm to measure consistency between extracted models from big dataset and predicting applicability of rule extraction

Many advancement is made in recent days and number of techniques are proposed by different researchers for processing and extracting knowledge from big data. But to evaluate the consistency in extracted model is always questionable. In this paper we are presenting two techniques for measuring the consistency between extracted model and predicting their applicability. In this paper, Meta learning based approach using characteristics of dataset is designed through which it can be identified whether the rule extraction technique will going to produce a better model as compare to conventional algorithm. Meta learning is concerned to identify the relationship between learning techniques and different big datasets. The proposed model is very generic and can be used in many different problems.