Improving Time Complexity and Accuracy of the Machine Learning Algorithms Through Selection of Highly Weighted Top k Features from Complex Datasets

Machine learning algorithms (MLAs) usually process large and complex datasets containing a substantial number of features to extract meaningful information about the target concept (a.k.a class). In most cases, MLAs suffer from the latency and computational complexity issues while processing such complex datasets due to the presence of lesser weight (i.e., irrelevant or redundant) features. The computing time of the MLAs increases explosively with increase in the number of features, feature dependence, number of records, types of the features, and nested features categories present in such datasets. Appropriate feature selection before applying MLA is a handy solution to effectively resolve the computing speed and accuracy trade-off while processing large and complex datasets. However, selection of the features that are sufficient, necessary, and are highly co-related with the target concept is very challenging. This paper presents an efficient feature selection algorithm based on random forest to improve the performance of the MLAs without sacrificing the guarantees on the accuracy while processing the large and complex datasets. The proposed feature selection algorithm yields unique features that are closely related with the target concept (i.e., class). The proposed algorithm significantly reduces the computing time of the MLAs without degrading the accuracy much while learning the target concept from the large and complex datasets. The simulation results fortify the efficacy and effectiveness of the proposed algorithm.