Special issue on Machine learning approaches and challenges of missing data in the era of big data

Excerpt

With the proliferation of mobile computing technology in the rapidly growing IoT community we are bombarded with wide variety of data. As information and technology ear is gone and now it’s for Big Data era were the questions arise about the veracity of the data that are generated. Thus these data are said to be ‘missing at random’ if the fact that they are missing is unrelated to actual values of the missing data Missing at Random: there is a pattern in the missing data but not on your primary dependent variables such as likelihood to recommend, once the data is missed it is vital to recover it by means of various machine learning methods and techniques as we have the historic data and its pattern. Missing Completely at Random: there is no pattern in the missing data on any variables. Many new techniques have offered very robust and hi-tech solutions for missing data and information analysis, collection, storage. Things get complicated with enormous amounts of valuable data in various formats. Since data are missed completely at random various data mining scheme and technique can be used to perform the task of data recovery. But still there is a challenge of fidelity of the data, how accurate are the data and how to verify its truthfulness and conformity of the facts. So data mining and AI based systems shall be used to evaluate the system. Today’s scientists are trying to solve this issue with variety of new techniques and to analyze this data to help them understand their operations and management of data. …