A Review of Hybrid Machine Learning approaches for handling Uncertainty in Data analysis - Maitra, S; Madan, S; Kandwal, R (International Journal of Computer Sciences and Engineering, 2025)
The advent of machine learning has brought about a revolution and has become key to classification and prediction problems encompassing a variety of application domains. There are a number of machine learning techniques with Naïve Bayes, Artificial Neural Networks (ANN) and decision tree being the more popular ones. This paper reviews the aforementioned machine learning algorithms in terms of their suitability for particular problem domains. It presents a comprehensive discussion on the strengths and weaknesses of each of these machine learning algorithms. Uncertainty prevails in data as learning data is usually imprecise, incomplete or noisy. The uncertainities in data mining affect the quality of results which are based on the data.The traditional data mining approaches are not suitable to handle some forms of uncertainty and vagueness. Several forms of vagueness and ambiguities are handled successfully by hybrid machine learning techniques.The paper further studies the efficacy of hybrid machine learning algorithms used in different application domains. It presents a discusssion on how uncertainty in data analyses can be addressed in an effective manner by the usage of hybrid machine learning techniques.