Implementation of cuckoo search algorithm for support vector machine parameters optimization in pre collision warning

Abstract

Nowadays, pre-collision warning is one of the substantial aspects of the transportation sector. One of the steps to detect the collisions is by classifying and predicting the collisions. There are many supervised machine learning algorithms used, such as Least Square – Support Vector Machine (LS-SVM). Radial Based Function (RBF) is one of the LS-SVM kernels, which is a well-known method to support reliable performance. However, C and Gamma of its parameters are chosen randomly. This makes the performance of the classifier less optimal. To overcome that problem, this paper proposed a cuckoo search algorithm for optimizing two parameters to get optimal accuracy. The proposed approach is applied to 8437 transportation records and evaluated by using Accuracy. In addition, the performance of the proposed method is compared to other well-known meta-heuristic optimization algorithms, namely: Bat Algorithm (BA-SVM) and Firefly Algorithm (FA-SVM). Experimental results show that the Cuckoo Search Algorithm (CSA-SVM) yields the best performance for each of the 10-folds cross-validation by reaching 84.817% for accuracy, compared to the Bat Algorithm and Firefly Algorithm.

Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads

“…Mapping low-dimensional nonlinear data to high-dimensional spatial data by nonlinear transformation, the nonlinear support vector machine achieved great performance in the majority of the previous studies. To date, an increasing number of SVMs are optimized by nature-inspired metaheuristic methods to solve classification and prediction problems in engineering [32]. Yusen Liu et al proposed the GWO-SVM to predict the hitch of internal resistance and contact resistance of lead-acid batteries.…”

Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN

“…Consequently, it has a strong robustness and high search efficiency. However, in the process of optimization, it has been found that Cuckoo search algorithm emerges with some drawbacks such as slow convergence speed and long operation time [30]. In order to overcome the shortcomings of CS and improve the accuracy and efficiency of optimization, the concept of random weight is introduced to improve CS.…”