Multi-Label classification for Mining Big Data

In big data problems mining requires special handling of the problem under investigation to achieve accuracy and speed on the same time. In this research we investigate the multi-label classification problems for better accuracy in a timely fashion. Label dependencies are the biggest influencing factor on performance, directly and indirectly, and is a distinguishing factor for multi-label from multi-class problems. The key objective in multi-label learning is to exploit this dependency effectively. Most of the current research ignore the correlation between labels or develop complex algorithms that don’t scale efficiently with large datasets. Hence, the goal of our research is to propose a fundamental solution through which preliminary identification of dependencies and correlations between labels is explicit from large multi-label datasets. This is to be done before any classifiers are induced by using an association rule mining algorithm. Then the dependencies discovered in the previous step are used to divide the problem into subsets depending on the correlation between labels for parallel classification. The experimental results were evaluated using Accuracy, Hamming Loss, MicroF-Measure and Subset Accuracy on a variety of datasets. The proposed model exploits all correlations among labels in multi-label datasets easily, facilitating the process of multi-label classification, increasing accuracy and performance as time was decreased while achieving higher accuracy. Keywords-Multi-Label classification; data mining; big data analytics;