UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

Metaheuristic Ensemble Pruning via Greedy-based Optimization Selection

Ensemble selection is a crucial problem for ensemble learning (EL) to speed up the predictive model, reduce the storage space requirements and to further improve prediction accuracy. Diversity among individual predictors is widely recognized as a key factor to successful ensemble selection (ES), while the ultimate goal of ES is to improve its predictive accuracy and generalization of the ensemble. Motivated by the problems stated in previous, we have devised a novel hybrid layered based greedy ensemble reduction (HLGER) architecture to delete the predictor with lowest accuracy and diversity with evaluation function according to the diversity metrics. Experimental investigations are conducted based on benchmark time series data sets, support vectors regression algorithm utilized as base learner to generate homogeneous ensemble, HLGER uses locally weight ensemble (LWE) strategies to provide a final ensemble prediction. The experimental results demonstrate that, in comparison with benchmark ensemble pruning techniques, HLGER achieves significantly superior generalization performance.

Predicting Inflation Component Drivers in Nigeria: A Stacked Ensemble Approach

Our study examined the disaggregation of inflation components in Nigeria using the stacked ensemble approach, a machine learning algorithm capable of compensating the weakness of a base learner with the strength of another. This approach gives flexibility of a synergistic performance of stacking each base learner and produces a formidable model that yields the highest level of accuracy and best predictive ability. We analyzed the test data, out-of-sample, and our results show a strong accuracy in predicting inflation. Our results further show that food CPI is the most important driver for headline, urban, and rural inflation while bread and cereals is the most important driver for food inflation. However, biscuits, agric rice, garri white are among the top main drivers of bread and cereal inflation. We note that some CPI items that mostly drive inflation have lower weights while others have higher weights therefore, focusing entirely on CPI weights as a policy guide will stymied a successful control of inflation in Nigeria. In addition, ignoring CPI items with lower weights in policy intervention will make inflation difficult to control. Above all, adequate trace of the source of inflation to the least sub-component of each component will help address or formulates an appropriate policy to confront inflation problems in Nigeria.JEL: C53, E37

Q-stacking prediction model considering wind speed error correction

In order to improve the accuracy of wind speed forecasting in wind farms, an ensemble-enhanced combined forecasting model is proposed considering error correction. First establish five independent base learners, build a two-layer Stacking ensemble model to fuse the prediction results of each base learner, and divide the input data by cross-validation to improve the generalization ability of the model. Then use the model-free learning framework Q learning selects the optimal model in the base learner to correct the preliminary prediction error and obtain the final prediction result. Select the actual wind farm measured data in different seasons to simulate the prediction effect of the model, and verify the prediction ability of the proposed model through comparative analysis. The results show that the model has high prediction accuracy with ε = 0.093.

A Method of Enterprise Financial Risk Analysis and Early Warning Based on Decision Tree Model

At present, the domestic and foreign financial crisis early-warning model research will provide only prediction accuracy as the only standard of success for early-warning model, ignoring an important problem, namely, will the financial crisis early-warning model for normal business, compared with the normal enterprise, forecast the financial crisis? This paper reviews the research situation at home and abroad from the perspective of the definition of the enterprise financial crisis, the form of expression, and so on. From the theoretical level, the relationship between the cause of the financial crisis and the change of financial indicators is established by explaining the early-warning theory, early-warning theory of financial crisis, and cost-sensitive learning theory, and the framework of early warning modeling of financial crisis based on decision tree is put forward. The decision tree model is constructed on several training subsets as the base learner so that the decision tree base learner can learn the characteristics of the healthy sample and crisis sample roughly equally. Taking the bond issuing enterprises of manufacturing industry as samples, the empirical comparison shows that the financial warning model based on decision tree integration is more accurate, which indicates that the model can improve the correct identification rate of financial crisis enterprises under the premise of higher overall warning accuracy.

On the Intrinsic Differential Privacy of Bagging

Differentially private machine learning trains models while protecting privacy of the sensitive training data. The key to obtain differentially private models is to introduce noise/randomness to the training process. In particular, existing differentially private machine learning methods add noise to the training data, the gradients, the loss function, and/or the model itself. Bagging, a popular ensemble learning framework, randomly creates some subsamples of the training data, trains a base model for each subsample using a base learner, and takes majority vote among the base models when making predictions. Bagging has intrinsic randomness in the training process as it randomly creates subsamples. Our major theoretical results show that such intrinsic randomness already makes Bagging differentially private without the needs of additional noise. Moreover, we prove that if no assumptions about the base learner are made, our derived privacy guarantees are tight. We empirically evaluate Bagging on MNIST and CIFAR10. Our experimental results demonstrate that Bagging achieves significantly higher accuracies than state-of-the-art differentially private machine learning methods with the same privacy budgets.

Research on PMF Model Based on BP Neural Network Ensemble Learning Bagging and Fuzzy Clustering

Probability matrix factorization model can be used to solve the problem of high-dimensional sparsity of user and rating data in the recommender systems. However, most of the existing methods use the user to model the item rating, ignoring the relationship between the user and the item, so the accuracy of user-item rating prediction is still low. Therefore, this paper proposes a probabilistic matrix factorization model based on BP neural network ensemble learning, bagging, and fuzzy clustering. Firstly, the membership function of fuzzy clustering and the selection of cluster center are used to calculate the user-item rating matrix; secondly, BP neural network trains the user-item scoring matrix after clustering, further improving the accuracy of scoring prediction; finally, the bagging method in ensemble learning is introduced, which takes the number of user-item scores as the base learner, trains the base learner through BP neural network, and finally obtains the score prediction through the voting results, which improves the stability of the model. Compared with the existing PMF models, the root mean square error of the PMF model after fuzzy clustering is increased by 9.27% and 3.95%, and the average absolute error is increased by 21.14% and 1.11%, respectively; then, the performance of the first mock exam is introduced. The root mean square error of the ensemble method is increased by 4.02% and 0.42%, respectively, compared with the existing single model. Finally, the weights of BP neural network training based learner are introduced to improve the accuracy of the model, which also verifies the universality of the model.

Enhancing accuracy and interpretability of machine learning models using super learning and permutation feature importance techniques in digital soil mapping

<p>The digital soil mapping (DSM) approach predicts soil characteristics based on the relationship between soil observations and related covariates using machine learning (ML) models. In this research, we applied a wide range of machine learning models (12 base learners) to predict and map soil characteristics. To enhance accuracy and interpretability we combined the base learner predictions using super learning strategy. However, a major problem of using super learning and complex models is that the explicit share of individual covariates persons in the overall result cannot be explicitly quantified. To overcome this restriction and make the super learning models interpretable, we employed model-agnostic interpretation tools, for example, permutation feature importance. Particularly, we integrated the weight assigned to each ML base learner obtained by super learning and the ranked ML base learner’s covariates obtained by permutation feature importance to explore the contribution of covariates on the final prediction. We tested our super learning and permutation feature importance techniques to predict and mapping physicochemical soil characteristics of Urmia Playa Lake (UPL) sediments in Iran. As expected, our results indicated that super leaning could significantly improve the ML accuracies for predicting soil characteristics of single base learners. In terms of root mean square error, super learning improved over the performance of the linear regression by an average of 45.7%. Furthermore, the permutation feature importance allowed us to interpret our results better and prove the significant contribution of geomorphological features and groundwater data in predicting soil characteristics of UPL sediments.</p>

Hybrid vs Ensemble Classification Models for Phishing Websites

Phishing is an internet crime achieved by imitating a legitimate website of a host in order to steal confidential information. Many researchers have developed phishing classification models that are limited in real-time and computational efficiency. This paper presents an ensemble learning model composed of DTree and NBayes, by STACKING method, with DTree as base learner. The aim is to combine the advantages of simplicity and effectiveness of DTree with the lower complexity time of NBayes. The models were integrated and appraised independently for data training and the probabilities of each class were averaged by their accuracy on the trained data through testing process. The present results of the empirical study on phishing website dataset suggest that the ensemble model significantly outperformed the hybrid model in terms of the measures used. Finally, DTree and STACKING methods showed superior performances compared to the other models.