Evolutionary Multilabel Classification Algorithm Based on Cultural Algorithm

Abstract

As one of the common methods to construct classifiers, naïve Bayes has become one of the most popular classification methods because of its solid theoretical basis, strong prior knowledge learning characteristics, unique knowledge expression forms, and high classification accuracy. This classification method has a symmetry phenomenon in the process of data classification. Although the naïve Bayes classifier has high classification performance in single-label classification problems, it is worth studying whether the multilabel classification problem is still valid. In this paper, with the naïve Bayes classifier as the basic research object, in view of the naïve Bayes classification algorithm’s shortage of conditional independence assumptions and label class selection strategies, the characteristics of weighted naïve Bayes is given a better label classifier algorithm framework; the introduction of cultural algorithms to search for and determine the optimal weights is proposed as the weighted naïve Bayes multilabel classification algorithm. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in classification performance.

Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

“…Data mining procedures in material, civil, and pavement engineering, in particular, have been reported widely in the previous two decades, thanks to the swift development in the approaches of ML [27]. The soft computing methods (SCMs) or artificial intelligence techniques (AITs) that are developed recently, for example, artificial neural networks (ANNs) (sub-types are; multilayer perceptron neural network (MLPNN), Bayesian neural network (BNN), general regression neural network (GRNN), backpropagation neural network (BPNN), and k-nearest neighbor (KNN)), ANNs with their hybrid form, i.e., support vector machine (SVM), multivariate adaptive regression splines (MARS), eXtreme gradient boosting (XGBoost), adaptive neuro-fuzzy inference system (ANFIS), alternate decision trees), genetic algorithms (GAs), M5 model trees, evolutionary algorithms (EAs), ensemble random forest regression (ERFR), genetic expression programming (GEP), and MEP, have facilitated in the development of the various models in conjunction with conventional statistical models, e.g., regression, among many others [25,[28][29][30][31][32][33][34][35][36][37][38]. Mechanistic learning has been frequently used to evaluate the estimating models for the development of intelligent structures [39].…”