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One of the challenges of data mining is finding hyperparameters for a learning algorithm that will produce the best model for a given dataset. Hyperparameter optimization automates this process, but it can still take significant time. It has been found that hyperparameter optimization does not always result in induced models with significant improvement over default values, yet no systematic analysis...
People today spend a lot of their time on the internet and a majority of which is spent on surfing various social networks like Facebook, twitter, Instagram etc. So much time is spent online on these social hubs that our opinions, about almost everything seems to be influenced by the larger opinion formed up in these social networks. In this project, opinion mining due to social swarming is discovered...
Occupational accident is a serious issue for every industry. Steel industry is considered to be one of the economic sectors having a high number of accidents. Thus, the main aim of this study is to build a model which could predict the occupational incidents (i.e., injury, near-miss, and property damage) using support vector machine (SVM) by utilizing a database comprising almost 5000 occupational...
The concept lattice is adopted as a tool of attribute reduction to reduce the redundant factors affecting coke ratio in this paper. On this basis, in order to solve the blindness and random problems in the parameters of artificial selection in support vector machine (SVM), this paper adopts genetic algorithm to optimize the penalty parameter C, kernel function parameters γ and insensitive loss coefficient...
The importance of the hyper parameters selection for a kernel-based algorithm, viz. Least Squares Support Vector Machines (LSSVM) has been a critical concern in literature. In order to meet the requirement, this work utilizes a variant of Artificial Bee Colony (known as mABC) for hyper parameters selection of LSSVM. The mABC contributes in the exploitation process of the artificial bees and is based...
Futures price forecasting based on least squares support vector machine is presented in the paper. In order to improve the prediction performance of least squares support vector machine, the experimental data can be normalized and appropriate parameters are selected by genetic algorithm. Least squares support vector machine is used to create the prediction model for futures price, and BP neural network...
Ensemble learning is a method to improve the performance of classification and prediction algorithms. It has received considerable attention because of its prominent generalization and performance improvement. However, its performance can be degraded due to multicollinearity problem where multiple classifiers of an ensemble are highly correlated with. This paper proposes genetic algorithm-based coverage...
Based on SVM (Support Vector Machine) theory, and the model to predict air conditioning load was established. In order to optimize the behavior of SVM, the DE (Differential Evolution) algorithm was introduced into classic SVM. The DE-SVM model is applied to a real example. The comparisons between the predicted results of the three models-GA (Genetic Algorithm) model, ACO (Ant Colony Optimization)...
This parameters selection is an important issue in the research of ??-support vector regression machine (??-SVRM), whose nature is an optimization selection process. Motivated by the effectiveness of differential evolution (DE) algorithm on optimization problem, a new automatic searching method based on DE algorithm was proposed. Experimental results demonstrate that ??-SVRM model optimization based...
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