Heuristic model to improve Feature Selection based on Machine Learning in Data Mining

Data Mining and Machine Learning is one of the most popular research areas in computer science that is relevant in today's world of unfathomable data. To keep up with the rising size of data, there arises a need to quickly extract knowledge from data sources to aid data analysis research and improve industry and market needs. Primary Data Mining algorithms like k-means, Apriori, PageRank etc. are used today, but Machine Learning techniques can enhance the same by learning from the complex patterns. This paper focuses on the various existing approaches where Machine Learning algorithms have been used to improve data classification and pattern recognition in Data Mining especially for Feature Selection. It compares and contrasts the existing techniques and finds out the best one among them. Further, the paper proposes a heuristic approach to theoretically overcome most of the limitations in existing algorithms.