Edited Nearest Neighbor Research Articles

Res-TranBiLSTM: An intelligent approach for intrusion detection in the Internet of Things

The Internet of Things (IoT), as the information carrier of the Internet and telecommunications networks, is a new network technology comprising physical entities embedded with electronic components, software and sensors, and characterized by strong complexity and openness. With the massive amount of data, the occurrence of network intrusion is also increasingly frequent, involving industrial control systems, IoT devices, mobile security, cloud services, and telecommunications services. With the diversification and intelligence of cyberattack behaviors, traditional intrusion detection systems (IDSs) face problems—such as insufficient feature extraction and inaccurate model classification—when faced with high-dimensional features and nonlinear massive data. Due to their powerful data representation learning ability, deep learning methods save substantial time in processing high-dimensional and complex intrusion data. On this basis, we propose an intrusion detection model using ResNet, Transformer and BiLSTM (Res-TranBiLSTM) that takes into account both the spatial and temporal features of network traffic. We use the Synthetic Minor Overriding Technique (SMOTE) – Edited Nearest Neighbor (ENN) method to alleviate the degree of data imbalance. In addition, we respectively establish a spatial feature extraction model based on ResNet and a temporal feature extraction model based on Transformer and BiLSTM to extract spatial features and temporal features parallelly. Finally, spatiotemporal features are included to achieve attack detection and classification. Further, simulation experiments are conducted using the public data sets NSL-KDD and CIC-IDS2017. The experiments are implemented using Python programming language and Pytorch framework. The results reveal that the performance of our proposed model is better than that of other models, with accuracy reaching 90.99%, 99.15% and 99.56%, on NSL-KDD dataset, CIC-IDS2017 dataset and MQTTset dataset, respectively. It increased the detection accuracy by about 1%-10% on NSL-KDD dataset and about 0.2%-10% on CIC-IDS2017 dataset, and about 1%-10% on MQTTset dataset. These results demonstrate that this method is effective in constructing and optimizing large-scale IDS in the IoT environment.

The Development of a Consensus Machine Learning Model for Hurricane Rapid Intensification Forecasts with Hurricane Weather Research and Forecasting (HWRF) Data

Abstract This study focused on developing a consensus machine learning (CML) model for tropical cyclone (TC) intensity-change forecasting, especially for rapid intensification (RI). This CML model was built upon selected classical machine learning models with the input data extracted from a high-resolution hurricane model, the Hurricane Weather Research and Forecasting (HWRF) system. The input data contained 21 or 34 RI-related predictors extracted from the 2018 version of HWRF (H218). This study found that TC inner-core predictors can be critical for improving RI predictions, especially the inner-core relative humidity. Moreover, this study emphasized the importance of performing resampling on an imbalanced input dataset. Edited nearest-neighbor and synthetic minority oversampling techniques improved the probability of detection (POD) by ∼10% for the RI class. This study also showed that the CML model has satisfactory performance on RI predictions compared to the operational models. CML reached 56% POD and 46% false alarm ratio (FAR), while the operational models had only 10%–30% POD but 50%–60% FAR. The CML performance on the non-RI classes was comparable to the operational models. The results indicated that, with proper and sufficient training data and RI-related predictors, CML has the potential to provide reliable probabilistic RI forecasts during a hurricane season.

Multi-view local hyperplane nearest neighbor model based on independence criterion for identifying vesicular transport proteins

Vesicular transport proteins participate in various biological processes and play a significant role in the movement of substances within cells. These proteins are associated with numerous human diseases, making their identification particularly important. In this study, we developed a novel strategy for accurately identifying vesicular transport proteins. We developed a novel multi-view classifier called graph-regularized k-local hyperplane distance nearest neighbor model (HSIC-GHKNN), which combines the Hilbert–Schmidt independence criterion (HSIC)-based multi-view learning method with a local hyperplane distance nearest-neighbor classifier. We first extracted protein evolution information using two feature extraction methods, pseudo-position-specific scoring matrix (PsePSSM) and AATP, and addressed dataset imbalance using the Edited Nearest Neighbors (ENN) algorithm. Subsequently, we employed a local hyperplane distance nearest-neighbor classifier for each view identification and added an HSIC term to maintain independence between views. We then assessed the performance of our identification strategy and analyzed the PsePSSM and AATP feature sets to determine the influencing factors of the classification results. The experimental results demonstrate that the accurate and Matthew correlation coefficients of our strategy on the independent test set are 85.8 % and 0.548, respectively. Our approach outperformed existing methods in most evaluation metrics. In addition, the proposed multi-view classification model can easily be applied to similar identification tasks.

Logistic Model Tree Forest for Steel Plates Faults Prediction

Fault prediction is a vital task to decrease the costs of equipment maintenance and repair, as well as to improve the quality level of products and production efficiency. Steel plates fault prediction is a significant materials science problem that contributes to avoiding the progress of abnormal events. The goal of this study is to precisely classify the surface defects in stainless steel plates during industrial production. In this paper, a new machine learning approach, entitled logistic model tree (LMT) forest, is proposed since the ensemble of classifiers generally perform better than a single classifier. The proposed method uses the edited nearest neighbor (ENN) technique since the target class distribution in fault prediction problems reveals an imbalanced dataset and the dataset may contain noise. In the experiment that was conducted on a real-world dataset, the LMT forest method demonstrated its superiority over the random forest method in terms of accuracy. Additionally, the presented method achieved higher accuracy (86.655%) than the state-of-the-art methods on the same dataset.

A machine learning and explainable artificial intelligence triage-prediction system for COVID-19

COVID-19 is a respiratory disease caused by the SARS-CoV-2 contagion, severely disrupted the healthcare infrastructure. Various countries have developed COVID-19 vaccines that have effectively prevented the severe symptoms caused by the virus to a certain extent. However, a small section of people continues to perish. Artificial intelligence advances have revolutionized healthcare diagnosis and prognosis infrastructure. In this study, we predict the severity of COVID-19 using heterogenous Machine Learning and Deep Learning algorithms by considering clinical markers, vital signs, and other critical factors. This study extensively reviews various classifier architectures to predict the COVID-19 severity. We built and evaluated multiple pipelines entailing combinations of five state-of-the-art data-balancing techniques (Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic, Borderline SMOTE, SMOTE with Tomek links, and SMOTE with Edited Nearest Neighbor (ENN)) and twelve heterogeneous classifiers such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Xgboost, Extratrees, Adaboost, Light GBM, Catboost, and 1-D Convolution Neural Network. The best-performing pipeline consists of Random Forest trained on Borderline SMOTE balanced data that produced the highest recall of 83%. We deployed Explainable Artificial Intelligence tools such as Shapley Additive Explanations and Local Interpretable Model-agnostic Explanations, ELI5, Qlattice, Anchor, and Feature Importance to demystify complex tree-based ensemble models. These tools provide valuable insights into the significance of critical features in the severity prediction of a COVID-19 patient. It was observed that changes in respiratory rate, blood pressure, lactate, and calcium values were the primary contributors to the increase in severity of a COVID-19 patient. This architecture aims to be an explainable decision-support triaging system for medical professionals in countries lacking advanced medical technology and infrastructure to reduce fatalities.

Identification of Essential Protein Using Chemical Reaction Optimization and Machine Learning Technique.

For the survival, development, and reproduction of the organism, understanding the working process of the cell, disease study, design drugs, etc. essential protein plays a crucial role. Due to a large number of biological information, computational methods are becoming popular in recent times to identify the essential protein. Many computational methods used machine learning techniques, metaheuristic algorithms, etc. to solve the problem. The problem with these methods is that the essential protein class prediction rate is still low. Many of these methods have not considered the imbalance characteristics of the dataset. In this paper, we have proposed an approach to identify essential proteins using a metaheuristic algorithm named Chemical Reaction Optimization (CRO) and machine learning method. Both topological and biological features are used here. The Saccharomyces cerevisiae (S. cerevisiae) and Escherichia coli (E. coli) datasets are used in the experiment. Topological features are calculated from the PPI network data. Composite features are calculated from the collected features. Synthetic Minority Over-sampling Technique and Edited Nearest Neighbor (SMOTE+ENN) technique is applied to balance the dataset and then the CRO algorithm is applied to achieve the optimal number of features. Our experiment shows that the proposed approach gives better results in both accuracy and f-measure than the existing related methods.

Comparative analysis of resampling algorithms in the prediction of stroke diseases

Stroke disease is a serious cause of death globally. Early predictions of the disease will save a lot of lives but most of the clinical datasets are imbalanced in nature including the stroke dataset, making the predictive algorithms biased towards the majority class. The objective of this research is to compare different data resampling algorithms on the stroke dataset to improve the prediction performances of the machine learning models. This paper considered five (5) resampling algorithms namely; Random over Sampling (ROS), Synthetic Minority oversampling Technique (SMOTE), Adaptive Synthetic (ADASYN), hybrid techniques like SMOTE with Edited Nearest Neighbor (SMOTE-ENN), and SMOTE with Tomek Links (SMOTE-TOMEK) and trained on six (6) machine learning classifiers namely; Logistic Regression (LR), Decision Tree (DT), K-nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), and XGBoost (XGB). The hybrid technique SMOTE-ENN influences the machine learning classifiers the best followed by the SMOTE technique while the combination of SMOTE and XGB perform better with an accuracy of 97.99% and G-mean score of 0.99, and auc_roc score of 0.99. Resampling algorithms balance the dataset and enhanced the predictive power of machine learning algorithms. Therefore, we recommend resampling stroke dataset in predicting stroke disease than modeling on the imbalanced dataset.

How to detect propaganda from social media? Exploitation of semantic and fine-tuned language models.

Online propaganda is a mechanism to influence the opinions of social media users. It is a growing menace to public health, democratic institutions, and public society. The present study proposes a propaganda detection framework as a binary classification model based on a news repository. Several feature models are explored to develop a robust model such as part-of-speech, LIWC, word uni-gram, Embeddings from Language Models (ELMo), FastText, word2vec, latent semantic analysis (LSA), and char tri-gram feature models. Moreover, fine-tuning of the BERT is also performed. Three oversampling methods are investigated to handle the imbalance status of the Qprop dataset. SMOTE Edited Nearest Neighbors (ENN) presented the best results. The fine-tuning of BERT revealed that the BERT-320 sequence length is the best model. As a standalone model, the char tri-gram presented superior performance as compared to other features. The robust performance is observed against the combination of char tri-gram + BERT and char tri-gram + word2vec and they outperformed the two state-of-the-art baselines. In contrast to prior approaches, the addition of feature selection further improves the performance and achieved more than 97.60% recall, f1-score, and AUC on the dev and test part of the dataset. The findings of the present study can be used to organize news articles for various public news websites.

A hybrid Data-Driven framework for Spam detection in Online Social Network

Twitter is one of the most prominent online social networks(OSN) used by celebrities, politicians, ordinary people, and organizations to enhance their popularity and brand value. The popularity of Twitter makes it a prime target for spammers to attack. Earlier, spammers used to focus on email and web-search engines; therefore, extensive research in spam detection for email and web search engines has been done. However, the humongous traction of OSN has taken away the focus of spammers from email and web search engines towards OSN and micro-blogging sites. The study is inspired by the need to verify the legitimacy of a profile on OSN to avoid any fake information or rumors on OSN. This paper modulates a hybrid framework for spam detection on Twitter. The sampling algorithm SMOTE-ENN combines SMOTE and Edited Nearest Neighbors (ENN) to generate balanced data that is further fed to various deep learning classification techniques to identify whether the tweet is spam or ham. The efficacy and performance of various state-of-the-art algorithms are evaluated through simulation and are compared through various performance metrics to determine the best spam detection framework for OSN.

RFSE-GRU: Data Balanced Classification Model for Mobile Encrypted Traffic in Big Data Environment

With the widespread use of mobile technologies and the Internet, traffic in mobile networks is increasing. This situation has made the classification of traffic an important element for data security and network management. However, encryption of traffic in modern networks makes it difficult to classify traffic with traditional methods. In this study, a unique deep learning-based classification model is proposed for the classification of encrypted mobile traffic data. The proposed model is a classification model called RFSE-GRU, which combines the Gated Recurrent Units (GRU) algorithm, feature selection and data balancing. The features that are more meaningful in the classification process are determined by selecting the features with the Random Forest algorithm. In addition, Synthetic Minority Oversampling Technique (SMOTE) oversampling algorithm and Edited Nearest Neighbor (ENN) undersampling algorithm were used together to reduce the negative impact of data imbalance on classification performance. The study was carried out on Apache Spark big data platform in Google Colab environment. In the study, ISCX VPN-Non VPN and UTMobileNet2021 datasets were used. Binary and multiclass classifications were made for the ISCX VPN-Non VPN dataset, and multiclass classifications were made for the UTMobileNet2021 dataset by using various algorithms on the datasets. The proposed model has been compared with eleven different algorithms and hybrid methods. At the same time, the effect of data balancing and feature selection on classification performance is examined. As a result, the proposed model achieved 93.91%, 82.68% and 96.83% accuracy rates in ISCX VPN-Non VPN binary and multiclass, UTMobileNet2021 multiclass classifications, respectively.

A hybrid sampling algorithm combining synthetic minority over-sampling technique and edited nearest neighbor for missed abortion diagnosis

BackgroundClinical diagnosis based on machine learning usually uses case samples as training samples, and uses machine learning to construct disease prediction models characterized by descriptive texts of clinical manifestations. However, the problem of sample imbalance often exists in the medical field, which leads to a decrease in classification performance of the machine learning.MethodsTo solve the problem of sample imbalance in medical dataset, we propose a hybrid sampling algorithm combining synthetic minority over-sampling technique (SMOTE) and edited nearest neighbor (ENN). Firstly, the SMOTE is used to over-sampling missed abortion and diabetes datasets, so that the number of samples of the two classes is balanced. Then, ENN is used to under-sampling the over-sampled dataset to delete the "noisy sample" in the majority. Finally, Random forest is used to model and predict the sampled missed abortion and diabetes datasets to achieve an accurate clinical diagnosis.ResultsExperimental results show that Random forest has the best classification performance on missed abortion and diabetes datasets after SMOTE-ENN sampled, and the MCC index is 95.6% and 90.0%, respectively. In addition, the results of pairwise comparison and multiple comparisons show that the SMOTE-ENN is significantly better than other sampling algorithms.ConclusionRandom forest has significantly improved all indexes on the missed abortion dataset after SMOTE-ENN sampled.

Analysis of machine learning strategies for prediction of passing undergraduate admission test

This article primarily focuses on understanding the reasons behind the failure of undergraduate admission seekers using different machine learning (ML) strategies. An operative dataset has been equipped using the least significant attributes to avoid the complexity of the model. The procedure halted after obtaining 343 observations with ten different attributes. The predictions are achieved using six immensely used ML techniques. Stratified K-fold cross-validation is mentioned to measure the expertise of proposed models to unsighted data, and Precision, Recall, F-Measure, and AUC Score matrices are determined to assess the efficiency of each model. A comprehensive investigation of this article indicates that the resampling strategy derived from the combination of edited nearest neighbor (ENN) and borderline SVM-based SMOTE and SVM model achieved prominent performance. Additionally, the borderline SVM-based SMOTE and the Adaboost model performs as the second-highest performing model.

Applying machine learning methods to predict geology using soil sample geochemistry

In this study we compared various machine learning techniques that used soil geochemistry to aid in geologic mapping. We tested six different sampling methods (undersample, oversample, Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), SMOTE and Edited Nearest Neighbor (SMOTEENN), and SMOTE and Tomek links (SMOTETomek)). SMOTE performed best with ADASYN and SMOTETomek having slightly lower effectiveness. Nine machine learning algorithms (naïve Bayes, logistic regression, quadratic discriminant analysis, nearest neighbors, radial basis function support-vector machine, artificial neural network, random forest, AdaBoost classifier, and gradient boosting classifier) were compared and AdaBoost classifiers and gradient boosting classifiers were found to be most effective. Finally, we experimented with multiple classifier systems (MCS) testing different combinations of algorithms and various combinatorial functions. It was found that MCS can outperform individual models, and the best MCS combined nearest neighbors, radial basis function support-vector machine, artificial neural network, random forest, AdaBoost classifiers, and gradient boosting classifier, then applied a logistic regression to the probabilities output by the models. Ultimately, we created a tool that is able to adequately predict underlying geology in the study area using soil geochemistry.

The composition of time-series images and using the technique SMOTE ENN for balancing datasets in land use/cover mapping

Monitoring Land-use/land-cover (LULC) changes are a significant challenge for sustainable spatial planning, particularly in response to transformation and degenerative landscape processes. These disturbances lead to the vulnerability of inhabitants and habitat and climate changes and socio-economic development in the region. Several studies have proposed different methods and techniques to monitor the spatial and temporal changes of LULC. Machine learning is a more popular method. However, the problem of data imbalance is a significant challenge, and the classification results tend to bias the majority classes for unbalanced data. Therefore, this study's objective is to develop a state-of-the-art technique to reduce the problem of data imbalance in LULC classification in Vietnam based on machine learning and SMOTE (Synthesizing Minority Oversampling Technology) with Edited Nearest Neighbor (ENN). Various statistical indices, including Kappa and Accuracy, have been used to assess the performance for the classification of Land-use/cover. The results indicate that integrating oversampling and under-sampling with SMOTE ENN gave better overall accuracy and generalization. We also find that the expected proportion of chance agreement after oversampling is higher than before (Kappa score before and after oversampling is 0.905244 and 0.974379, respectively). This study provides an effective method to monitor spatial and temporal land cover change in Vietnam; it plays a role as a framework for other relevant research related to land cover change, which can support planning and sustainable management of the territory.

Early Prediction of Cerebrovascular Disease using Boosting Machine Learning Algorithms to Assist Clinicians

Clinicians are required to make an early prediction of diseases to save a life, especially cerebrovascular diseases. The objective of this research is to use mathematical models such as boosting machine learning algorithms as a tool to be applied by clinicians for cerebrovascular disease. This paper particularly, considered XGBoost, AdaBoost, LightGBM, and CatBoost Classifiers to predict cerebrovascular disease using age, gender, BMI, hypertension, heart disease, residence type, ever married, smoking status, and average glucose level of the patients. Synthetic Minority Over-Sampling Technique Edited Nearest Neighbors Under-sampling (SMOTE-ENN) and Feature Engineering were applied to the dataset to enhance the performance of the algorithms. The result obtained showed that XGBoost Classifier is the best model with an accuracy of 98% and an AUC of 0.983.

Intrusion Detection System Using K-Means and Edited Nearest Neighbour Algorithm

In imbalanced network traffic, malicious cyber-attacks can often hide in large amounts of normal data. It exhibits a high degree of stealth and obfuscation in cyberspace, making it difficult for Network Intrusion Detection System (NIDS) to ensure the accuracy and timeliness of detection. This paper researches machine learning and deep learning for intrusion detection in imbalanced network traffic. It proposes a novel Difficult Set Sampling Technique (DSSTE) algorithm to tackle the class imbalance problem. First, use the Edited Nearest Neighbor (ENN) algorithm to divide the imbalanced training set into the difficult set and the easy set. Next, use the K- Means algorithm to compress the majority samples in the difficult set to reduce the majority. Zoom in and out the minority samples’ continuous attributes in the difficult set synthesize new samples to increase the minority number. Finally, the easy set, the compressed set of majority in the difficult, and the minority in the difficult set are combined with its augmentation samples to make up a new training set. The algorithm reduces the imbalance of the original training set and provides targeted data augment for the minority class that needs to learn. It enables the classifier to learn the differences in the training stage better and improve classification performance. To verify the proposed method, we conduct experiments on the classic intrusion dataset NSL-KDD. We use classical classification models: random forest(RF), Support Vector Machine (SVM), XGBoost, Long and Short- term Memory (LSTM), Adaboost, AlexNet, Mini- VGGNet.

Classification of imbalanced medical data: An empirical study of machine learning approaches

Thousands of patients around the world affecting their health with various factor as age, body mass index, cholesterol levels, albumin levels and several other factor. Prediction of health outcome due to these factors at a proper time can be served as an early warning. Recent growth in machine learning algorithm inspired us to build a predictive model for better healthcare facilities. In our work we have focused on problem of noisy and imbalanced dataset in which majority class is favored over minority one that leads to false prediction. We have experimented with two publicly available medical imbalanced dataset which varies in its size as MIT’s GOSSIS death and PIMA Indians Diabetes Dataset based on binary class. In this model we have investigated 3 oversampling techniques (Synthetic Minority Oversampler, Random Oversampler and Adaptive Synthetic Sampler) along with two undersampling techniques (Random Undersampler and Near Miss) which were paired with 3 data reduction and cleaning methods namely Tomek Links, One Sided Selection and Edited Nearest Neighbors. At last, we found that combination of Adaptive Synthetic Sampler along with One Sided Selection perform better in case of large size dataset while combination of random oversampler along with Tomek Link showed better performance in case of low size data dataset. We have also analyzed that oversampling technique gives quite promising results in comparison to undersampling methods specifically when applied with machine learning classifiers as these classifiers are data hungry algorithms.

Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology.

Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning approaches have been used to predict toxicity-related biological activities using chemical structure descriptors. However, toxicity-related proteomic features have not been fully investigated. In this study, we construct a computational pipeline using machine learning models for predicting the most important protein features responsible for the toxicity of compounds taken from the Tox21 dataset that is implemented within the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) therapeutic discovery platform. Tox21 is a highly imbalanced dataset consisting of twelve in vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For the machine learning model, we employed a random forest with the combination of Synthetic Minority Oversampling Technique (SMOTE) and the Edited Nearest Neighbor (ENN) method (SMOTE+ENN), which is a resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR) and the mitochondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUCROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were analyzed for enrichment to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidates significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong for twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.

Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions

This research is aimed at predicting the physical stability for amorphous solid dispersion by utilizing deep learning methods. We propose a prediction model that effectively learns from a small dataset that is imbalanced in terms of class. In order to overcome the imbalance problem, our model performs a hybrid sampling which combines synthetic minority oversampling technique (SMOTE) algorithm with edited nearest neighbor (ENN) algorithm and reduces the dimensionality of the dataset using principal component analysis (PCA) algorithm during data preprocessing. After the preprocessing, it performs the learning process using a carefully designed neural network of simple but effective structure. Experimental results show that the proposed model has faster training convergence speed and better test performance compared to the existing DNN model. Furthermore, it significantly reduces the computational complexity of both training and test processes.

An explainable XGBoost model improved by SMOTE-ENN technique for maize lodging detection based on multi-source unmanned aerial vehicle images

Remote sensing image is becoming an increasingly popular tool for crop lodging detection because it conveniently provides features for building machine learning models and predicting lodging. However, difficulties in interpreting machine learning models and their predictions limit the confidence of using remote sensing images to detect lodging. In addition, the lodging datasets used for modeling are difficult to balance under natural conditions. Designing a robust and interpretable classification model for the detection of lodging in an imbalanced distribution dataset poses a particularly difficult challenge. In this study, visible and multi-spectral images were collected with a UAV to extract relevant features from remote sensing images. In a preliminary step, Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) method were used to treat imbalanced datasets. The SMOTE-ENN-XGBoost model is proposed for the efficient identification of maize lodging at the plot scale. The SMOTE-ENN-XGBoost model achieved an F1-score of 0.930 and a recall of 0.899 on a testing set, suggesting that it can be used for modeling lodging detection. Additionally, the SHapley Additive exPlanations (SHAP) approach was employed to interpret the identification and prioritization of features that determine lodging classification and activity prediction. The results showed that canopy structure and textural features are relatively stable compared with spectral features, which are susceptible to the external environment when modeling is employed to detect lodging. This work also showed that canopy structural, spectral, and textural information should be considered simultaneously rather than separately when detecting crop lodging in a crop breeding program in order to prevent differences in expression controlled by the interaction between genotype and environment obscuring the change in a single feature before and after lodging. For practical applications of machine learning models in crop lodging detection, such insights are of critical relevance. Taken together, the results of this study encourage further applications of remote sensing techniques to build interpretable machine learning models.