ABSTRAT:
Support vector regression (SVR) is one of the most powerful and widely used machine learning algorithms regarding prediction. The kernel type, penalty factor and other parameters influence the efficiency and performance of SVR deeply. The optimization of these parameters is held a hot issue. In this work, we propose a SVR based prediction approach using henry gas solubility optimization algorithm (HGSO), which is a recent meta-heuristic algorithm inspired by Henry’s law. First, SVR parameters are randomly generated in some certain ranges to form parameter population. Second, the prediction accuracies (PAs) are obtained using the population and SVR. Thirdly, the population and optimal SVR parameters are updated via PAs and HGSO. We repeat the second and third steps until the cut-off conditions are met. Ten low- and high-dimensional benchmark data sets are utilized to assess the prediction accuracy, convergence performance and computational complexity of the presented approach and other well-known algorithms. The experimental results reveal that our approach has the optimum comprehensive performance
EXISTING SYSTEM :
Motivated by these issues, this work is an attempt to fill the gaps, and makes contributions in the following two areas. First, a novel swarm intelligence optimization approach is sought to optimize SVR parameters and obtain good prediction accuracy, convergence performance and computational complexity with relatively small predictive stability loss. Second, many data sets from various fields are utilized to test the proposed approach. Henry gas solubility optimization (HGSO) algorithm is an up-to-date meta-heuristic method [16], which was invented by Hashim et al. [17], [18] in 2019. It simulates the behaviour of Henry’s law and imitates the huddling behaviour of gas to balance exploitation and exploration in the search space. HGSO algorithm has evaluated on several benchmark data sets and achieved significant superiority against some competitive algorithms. In addition, it has little influence on SVR prediction stability via subsequent experiments
EXISTING SYSTEM DISADVANTAGES:
1.LESS ACCURACY
2. LOW EFFICIENCY
PROPOSED SYSTEM :
the prediction accuracy and convergence stability, the proposed SVR-HGSO approach is compared with SVR- HHO, SVR-SSA, SVR-DA, SVR-ALO, SVR-PSO, SVR-FA and single -SVR based on the ten data sets. The initial parameter setting of SVR-HGSO and other algorithms are represented in Table 2. The 10-folds cross-validation training/testing technology is applied. The ratio of training data to testing data was 9 to 1. The optimal prediction accuracy (MSE) is obtained via the steps of Section 3 based on the training and testing data. This process is repeated ten times for each algorithm. The average (Avg) and standard deviation (Std) of ten optimal prediction accuracies are revealed
PROPOSED SYSTEM ADVANTAGES:
1.HIGH ACCURACY
2.HIGH EFFICIENCY
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
