Hybridized classification algorithms for data classification applications: A review

Abstract

Machine-based classification usually involves some computer programs, known as algorithms, developed using several mathematical formulations to accelerate the automated classification process. Along with the exponential increase in the size and computational complexity of the data today, such optimized, robust, agile and reliable computational algorithms are required which can efficiently carry out these conforming classification tasks. In this review paper, deterministic optimization techniques have been analysed that are efficiently employed for machine learning applications. In this review, systematic literature review approach has been adopted in which 200 research articles were downloaded from which 100 latest articles has been selected based on the most commonly employed neural networks’ techniques. Moreover, the reported neural networks techniques based on Back Propagation Neural Network (BPNN), Recurrent Neural Networks (RNNs) Algorithm and Levenberg-Marquardt (LM) with several hybridized classification algorithms based on optimization techniques have been indicated that are commonly used to optimize and benefit the classification process.

Keywords

• Recurrent Neural Networks (RNNs) algorithm
• Back Propagation Neural Network (BPNN)
• Deterministic algorithms
• Machine learning
• Levenberg-Marquardt (LM)