Comparative Study of Ensemble Models of Deep Convolutional Neural Networks for Crop Pests Classification

Pest infestations on wheat, corn, soybean, and other crops can cause substantial losses to their yield. Early diagnosis and automatic classification of various insect pest categories are of considerable importance for accurate and intelligent pest control. However, given the wide variety of crop pests and the high degree of resemblance between certain pest species, the automatic classification of pests can be very challenging. To improve the classification accuracy on publicly available D0 dataset with 40 classes, this paper compares studies on the use of ensemble models for crop pests classification. First, six basic learning models as Xception, InceptionV3, Vgg16, Vgg19, Resnet50, MobileNetV2 are trained on D0 dataset. Then, three models with the best classification performance are selected. Finally, the ensemble models, i.e, linear ensemble named SAEnsemble and nonlinear ensemble SBPEnsemble, are designed to combine the basic learning models for crop pests classification. The accuracies of SAEnsemble and SBPEnsemble improved by 0.85% and 1.49% respectively compared to basic learning model with the highest accuracy. Comparison of the two proposed ensemble models show that they have different performance under different condition. In terms of performance metrics, SBPEnsemble giving accuracy of classification at 96.18%, is more competitive than SAEnsemble.

Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization

<span>Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment. However, among the ensemble models, little consideration has been focused on the hyper-parameters tuning of base learners, although these are crucial to constructing ensemble models. This study proposes an improved credit scoring model based on the extreme gradient boosting (XGB) classifier using Bayesian hyper-parameters optimization (XGB-BO). The model comprises two steps. Firstly, data pre-processing is utilized to handle missing values and scale the data. Secondly, Bayesian hyper-parameter optimization is applied to tune the hyper-parameters of the XGB classifier and used to train the model. The model is evaluated on four widely public datasets, i.e., the German, Australia, lending club, and Polish datasets. Several state-of-the-art classification algorithms are implemented for predictive comparison with the proposed method. The results of the proposed model showed promising results, with an improvement in accuracy of 4.10%, 3.03%, and 2.76% on the German, lending club, and Australian datasets, respectively. The proposed model outperformed commonly used techniques, e.g., decision tree, support vector machine, neural network, logistic regression, random forest, and bagging, according to the evaluation results. The experimental results confirmed that the XGB-BO model is suitable for assessing the creditworthiness of applicants.</span>

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”.