A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning

Abstract

Machine learning (ML) is a new-age thriving technology, which facilitates computers to read and interpret from the previously present data automatically. It makes use of multiple algorithms to build models, mathematical in nature, and then makes predictions for the new data using the past data and knowledge. Lately, it has been adopted for text detection, hate speech detection, recommender system, face detection, and more. In this paper, majorly all the aspects concerning five machine learning algorithms namely-K-Nearest Neighbor (KNN), Genetic Algorithm (GA), Support Vector Machine (SVM), Decision Tree (DT) , and Long Short Term Memory (LSTM) network have been discussed in great detail which is a prerequisite for venturing into the field of ML. This paper throws light on various new results and conclusions related to these algorithms via research and review of recently published papers that carried out quantitative and qualitative research on real-time problems, mainly predictive analytics in multidisciplinary fields. This paper also talks about the circumstantial origin of these algorithms, which although has been rarely talked about in previous publications, is a preeminent point of discussion for ML enthusiasts and amateurs, both. To explain and understand the accuracy, robustness, and reliability of the algorithms, they were exhaustively reviewed and researched in all aspects qualitatively and quantitatively, wherein the LSTM network and SVM algorithm have projected a superior behavior over the rest. The paper answers all relevant questions that may arise during the study of these algorithms ranging from their origin, to their definition, methodologies of execution, real-time applications attached with sufficient novel evidence, followed by the advantages and major trade-offs; lastly an elaborate comparison of their performances on quantitative and qualitative grounds has been presented. To conclude, the paper also highlights the future scope of ML algorithms and artificial intelligence in the coming times and their roles in automation and holistic development, not just in technology-related aspects but also, the humanitarian aspects, finally followed by reliable and relevant conclusions derived from this exhaustive research.