Spectrally Deconfounded Random Forests

Tumor-associated macrophages (TAMs) constitute up to 50% of tumor bulk in glioblastoma (GBM) and play an important role in tumor maintenance and progression. The recently discovered differences between invading tumour periphery and hypoxic tumor core implies that macrophage biology is also distinct by location. This may provide further insight into the observed treatment resistance to immune modulation. We hypothesize that macrophage activation occurs through processes that are distinct in tumor periphery versus core. We therefore investigated regional differences in TAM recruitment and evolution in GBM by combining open source single cell and bulk gene expression data. We used single cell gene expression data from 4 glioblastomas (total of 3589 cells) and 122 total bulk samples obtained from 10 different patients. Cell identity, ontogeny (bone-marrow derived macrophages-BMDM vs microglia), and macrophage activation state were inferred using verified gene expression signatures. We captured the spectrum of immune states using cell trajectory analysis with pseudotime ordering. In keeping with previous studies, TAMs carrying BMDM identity were more abundant in tumor bulk while microglia-derived TAMs dominated the tumor periphery across all macrophage activation states including pre-activation. We note that core TAMs evolve towards a pro-inflammatory state and identify a subpopulation of cells based on a gene program exhibiting strong, opposing correlation with Programmed cell Death-1 (PD-1) signaling, which may correlate to their response to PD-1 inhibition. By contrast, peripheral TAMs evolve towards anti-inflammatory phenotype and contains a population of cells strongly associated with NFkB signaling. Our preliminary analysis suggests important regional differences in TAMs with regard to recruitment and evolution. We identify regionally distinct and potentially actionable cell subpopulations and advocate the need for a multi-targeted approach to GBM therapeutics.

Multidate imagery and satellite image derivatives such as vegetation and texture indices have been reported to improve image classification. However, the increase in additional predictor variables has also resulted in high data dimensionality and redundancy. Feature selection and extraction can be used to reduce high data dimensionality and minimize redundancy. The purpose of this chapter is to test whether feature selection can improve image classification. In this chapter, image classification will be performed using two different approaches. First, image classification is performed using the random forests (RF) classifier and multiple data sets (that consist of multidate Landsat 5 TM imagery, and vegetation and texture indices). Second, image classification is performed using the RF classifier with feature selection and multiple data sets. While the tutorial exercises indicate that feature selection did not improve image classification accuracy, it reduced the number of predictor variables.

Abstract. Random Forest is a supervised learning algorithm developed from decision trees with the application of boostrap aggregating (bagging). This method grows trees from decision trees to produce a forest or the best model called the random forest model. Tree growth is done with randomly selected data with returns through the bagging process. Random forest is considered to provide better performance results for diabetes data among other supervised learning methods, because random forest and has the lowest error rate compared to other methods. Random forest is also an important technique for medical data classification, especially for diagnosing diabetics. In this study, classification was carried out using Pima Indian Diabetes data, which is an American tribe that lives in Arizona and Mexico. Classification analysis was carried out using an algorithm to see the level of accuracy in random forest classification on Pima Indian diabetes data. The results show that the accuracy value of random forest classification is 74.78%, this value is in the accuracy category at the fair classification level. In this random forest classification, there are three main variables that become importance variables, namely glucose then BMI, and age.
 Abstract. Random Forest is a supervised learning algorithm developed from decision trees with the application of boostrap aggregating (bagging). This method grows trees from decision trees to produce a forest or the best model called the random forest model. Tree growth is done with randomly selected data with returns through the bagging process. Random forest is considered to provide better performance results for diabetes data among other supervised learning methods, because random forest and has the lowest error rate compared to other methods. Random forest is also an important technique for medical data classification, especially for diagnosing diabetics. In this study, classification was carried out using Pima Indian Diabetes data, which is an American tribe that lives in Arizona and Mexico. Classification analysis was carried out using an algorithm to see the level of accuracy in random forest classification on Pima Indian diabetes data. The results show that the accuracy value of random forest classification is 74.78%, this value is in the accuracy category at the fair classification level. In this random forest classification, there are three main variables that become importance variables, namely glucose then BMI, and age.

Considering the form diversity of tree species composition in the Bagong Mountain National Forest Park of China, we mapped tree species utilizing Machine Learning Algorithms (support vector machines (SVM) and random forest (RF) classifiers) based on the OHS-2 hyperspectral satellite image by different datasets which combined spectral information and hyperspectral-derived vegetation indices (VIs) for improving tree species classification and explored the best performance of them. To verify the improvement, the results of physically-based spectral classifiers (spectral angle mapper (SAM) and maximum likelihood (ML) classifiers) were applied to compare with the results of machine learning algorithms. The results indicated an overall accuracy of 94.01%, 96.08%, 82.9% and 79.3% for SVM, RF, SAM and ML classifiers of the best performance using different datasets. Highest accuracies resulted from two machine learning algorithms classifiers; SVM and RF compared to SAM and ML classifiers. Although SVM outperformed RF when using all hyperspectral bands and VIs, the overall accuracy of the RF classifier is higher when compared to the SVM classifier using VIs combined selected features. Meanwhile, the RF classifier performed better than SVM after removing the redundancy of spectral data in training samples. Moreover, the machine learning algorithms successfully classified a small number of tree species (Cedrus deodara and Pterocarya stenoptera C. DC.) in the study area, but the physical spectroscopy-based method failed to classify these species. Such integration strategy improved the effectiveness of enhancing the accuracy of tree species classification and mapping their distribution on broad spatial and temporal scales using machine learning algorithms and hyperspectral imagery.

Even today, the assessment of avalanche danger is by large a subjective, yet data-based decision-making process. Human experts analyze heterogeneous data volumes, diverse in scale, and conclude on the avalanche scenario based on their experience. Nowadays, modern machine learning methods and the rise in computing power in combination with physical snow cover modelling open up new possibilities for developing decision support tools for operational avalanche forecasting. Therefore, we developed a fully data-driven approach to predict the regional avalanche danger level, the key component in public avalanche forecasts, for dry-snow conditions in the Swiss Alps. Using a large data set of more than 20 years of meteorological data measured by a network of automated weather stations, which are located at the elevation of potential avalanche starting zones, and snow cover simulations driven with these input weather data, we trained two random forest (RF) classifiers. The first classifier (RF #1) was trained relying on the forecast danger levels published in the avalanche bulletin. Given the uncertainty related to a forecast danger level as a target variable, we trained a second classifier (RF #2), relying on a quality-controlled subset of danger level labels. We optimized the RF classifiers by selecting the best set of input features combining meteorological variables and features extracted from the simulated profiles. The accuracy of the danger level predictions ranged between 74 % and 76 % for RF #1, and between 72 % and 78 % for RF #2, with both models achieving better performance than previously developed methods. To assess the accuracy of the forecast, and thus the quality of our labels, we relied on nowcast assessments of avalanche danger by well-trained observers. The performance of both models was similar to the accuracy of the current experience-based Swiss avalanche forecasts (which is estimated to 76 %). The models performed consistently well throughout the Swiss Alps, thus in different climatic regions, albeit with some regional differences. A prototype model with the RF classifiers was already tested in a semi-operational setting by the Swiss avalanche warning service during the winter 2020-2021. The promising results suggest that the model may well have potential to become a valuable, supplementary decision support tool for avalanche forecasters when assessing avalanche hazard.

The random forest (RF) classifier, which is a combination of tree predictors, is one of the most powerful classification algorithms that has been recently applied for fault detection and diagnosis (FDD) of industrial processes. However, RF is still suffering from some limitations such as the noncorrelation between variables. These limitations are due to the direct use of variables measured at nodes and therefore the only use of static information from the process data. Thus, this article proposes two enhanced RF classifiers, namely the Euclidean distance based reduced kernel RF (RK-RF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ED</sub> ) and K-means clustering based reduced kernel RF (RK-RF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Kmeans</sub> ), for FDD. Based on the kernel principal component analysis, the proposed classifiers consist of two main stages: feature extraction and selection, and fault classification. In the first stage, the number of observations in the training data set is reduced using two methods: the first method consists of using the Euclidean distance as dissimilarity metric so that only one measurement is kept in case of redundancy between samples. The second method aims at reducing the amount of the training data based on the K-means clustering technique. Once the characteristics of the process are extracted, the most sensitive features are selected. During the second phase, the selected features are fed to an RF classifier. An emulated grid-connected PV system is used to validate the performance of the proposed RK-RF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ED</sub> and RK-RF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Kmeans</sub> classifiers. The presented results confirm the high classification accuracy of the developed techniques with low computation time.

Lung cancer is one of the three deadliest diseases in the world and has rapidly developed. Based on this, researchers conducted research to predict the factors that influence lung cancer. One method to identify this is using data mining methods and classification techniques. Researchers used several popular algorithms in classification to make comparisons of the most accurate algorithms for lung cancer classification. The algorithms used include K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. The researcher used this algorithm because, in the research that the researcher found on the Kaggle platform, he examined the comparison of the algorithm using the breast cancer dataset. In previous studies, their researchers used SVM, which obtained an accuracy of 96.47%, Neural Networks of 97.06%, and Naïve Bayes with an accuracy of 91.18% to study breast cancer. The difference from previous research is that this study uses several existing algorithms in Machine Learning such as K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. In addition, this research was conducted to see whether the results of the accuracy of the algorithm that the researchers carried out using the lung cancer dataset had different results. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood.

Introduction: Chronic Obstructive Pulmonary Disease (COPD), is a condition caused by damage to theairways or other parts of the lung that blocks airflow and makes it hard to breathe [1]. COPD is the thirdleading cause of death worldwide, and the seventh leading cause of poor health worldwide [2]. Studieshave shown that 20–86% of people with COPD worldwide may be undiagnosed [3]. As there is currentlyno cure for COPD, early detection is the best option. Current Machine Learning (ML) models focus onusing chest images (CT or X-ray scans) to detect COPD; however, the scanning process can be unsafe forpatients with COPD [4].Methods: This study utilized an open data set containing various physical tests of 100 patients withCOPD. To train this model, the Random Forest (RF) classifier was used. The accuracy was then plottedon a graph.Results: The Random Forest classifier was able to achieve an accuracy of 92.41% with a perfect recallvalue of 1.00. This recall value indicates that the Random Forest classifier was able to correctly diagnoseall of the patients with COPD in this dataset.Discussion: This study developed a novel ML model that can accurately provide a diagnosis for COPD.Further studies could use this code with a larger dataset to obtain a higher accuracy. White individualshave been reported to have a higher prevalence of COPD [5]. By developing a dataset that accounts forrace, we will be able to obtain a more accurate diagnosis.

The objective of this study was to develop a machine learning (ML) application to determine the optimal dosage of sodium hypochlorite (NaOCl) to curtail corrosion and odor by H2 S in the headworks of a water resource recovery facility (WRRF) without overly consuming volatile fatty acids (VFAs) that are essential for the enhanced biological phosphorus removal. Given the highly diverse datasets available, three subproblems were formulated, and three cascaded ML modules were developed accordingly. The final ML models, chosen based on performance, were able to predict various targeted variables. More specifically, in Module 1, a recurrent neural network (RNN) was designed to predict wastewater characteristics. In Module 2, a random forest (RF) classifier and a support vector machine (SVM) classifier were built with the information from Module 1 along with other datasets to predict the concentrations of VFAs and H2 S, respectively. Finally, in Module 3, with the information obtained from Module 2, another RF classifier was developed to predict NaOCl dosage to reduce H2 S but keeping VFAs within the target range. These efforts are relevant and informative for WRRFs that are considering developing Intelligent Water Systems to predict the wastewater characteristics to make operational improvements. PRACTITIONER POINTS: A recurrent neural network (RNN) using long short-term memory (LSTM) successfully predicted influent wastewater parameters. A support vector machine classifier predicted hydrogen sulfide (H2 S) with 97.6% accuracy. The concentration of VFAs, an important parameter in EBPR, was predicted using a random forest classifier with 93.4% accuracy. The optimal NaOCl dosage for H2 S control can be predicted with a random forest classifier using H2 S, VFAs, and flow.

Diabetes claims the lives of thousands each year, and many individuals remain oblivious to their condition until it reaches a critical stage. This study presents a data mining-based approach aimed at enhancing the early detection and prediction of diabetes, utilizing data from the Pima Indian Diabetes dataset. Despite the adaptability of fuzzy C-Means for various data types, the ultimate outcome of the clustering process hinges on the initial placement of cluster centers. Additionally, precision in data clustering is crucial; it can furnish either extensive, well-grouped data for the random forest or limited data, constraining its efficacy. Our principal objective was to enhance the accuracy of fuzzy C-means clustering and the random forest. To boost the model's performance, we incorporated PCA, fuzzy c-means, and the Random Forest approach. Various algorithmic combinations were employed, and the results unequivocally demonstrate that our model surpasses the original outcomes of the Pima Indian Diabetes Dataset in terms of accuracy. The diabetic prediction model achieved a remarkable accuracy of 97.40\% through the utilization of PCA, logistic regression, and K-Means. However, when employing PCA in conjunction with fuzzy C-means and random forests, an even higher accuracy of 98.96\% was attained. Empirical evidence confirms that the implementation of PCA significantly enhanced the accuracy of both the fuzzy C-means clustering approach and the random forest classifier, deviating from previous findings. To improve the model's performance, we used PCA, fuzzy c-means, and the Random Forest approach. Different algorithm combinations were used, and the results clearly show that our model outperforms the original Pima Indian Diabetes Dataset outcomes in terms of accuracy. The diabetic prediction model was improved to 97.40% accuracy using PCA, logistic regression, and K -Means. Using PCA with fuzzy C-means and random forests, however, we achieved an accuracy of 98.96%. Based on empirical evidence, it has been demonstrated that the implementation of PCA improved the accuracy of the fuzzy C-means clustering approach and the random forest classifier. These findings differ from previous findings.

The Local Climate Zone (LCZ) classification is already widely used in urban heat island and other climate studies. The current classification method does not incorporate crucial urban auxiliary GIS data on building height and imperviousness that could significantly improve urban-type LCZ classification utility as well as accuracy. This study utilized a hybrid GIS- and remote sensing imagery-based framework to systematically compare and evaluate different machine and deep learning methods. The Convolution Neural Network (CNN) classifier outperforms in terms of accuracy, but it requires multi-pixel input, which reduces the output’s spatial resolution and creates a tradeoff between accuracy and spatial resolution. The Random Forest (RF) classifier performs best among the single-pixel classifiers. This study also shows that incorporating building height dataset improves the accuracy of the high- and mid-rise classes in the RF classifiers, whereas an imperviousness dataset improves the low-rise classes. The single-pass forward permutation test reveals that both auxiliary datasets dominate the classification accuracy in the RF classifier, while near-infrared and thermal infrared are the dominating features in the CNN classifier. These findings show that the conventional LCZ classification framework used in the World Urban Database and Access Portal Tools (WUDAPT) can be improved by adopting building height and imperviousness information. This framework can be easily applied to different cities to generate LCZ maps for urban models.

The increasing penetration of renewable energy into power systems has intensified transient power quality (PQ) disturbances, demanding efficient detection and classification methods to enable timely operational decisions. This paper introduces a hybrid framework combining an Improved Adaptive S-Transform (IAST) with a Random Forest (RF) classifier to address these challenges. The IAST employs a globally adaptive Gaussian window as its kernel function, which automatically adjusts window length and spectral resolution based on real-time frequency characteristics, thereby enhancing time–frequency localization accuracy while reducing algorithmic complexity. To optimize computational efficiency, window parameters are determined through an energy concentration maximization criterion, enabling rapid extraction of discriminative features from diverse PQ disturbances (e.g., voltage sags and transient interruptions). These features are then fed into an RF classifier, which simultaneously mitigates model variance and bias, achieving robust classification. Experimental results show that the proposed IAST–RF method achieves a classification accuracy of 99.73%, demonstrating its potential for real-time PQ monitoring in modern grids with high renewable energy penetration.

Brain signal variability has been proposed as an index of the brain’s cognitive capacity. In this work, we examined neural variability by calculating the standard deviation of single trial activation estimates during memory encoding in 30 patients with mild cognitive impairment (MCI) and 31 elderly controls. We deployed a random forest (RF) classifier, using variability maps as features to distinguish MCI patients from controls, and obtained classification accuracies of up to 86%. We then used partial least squares correlation to identify variability patterns associated with task performance and compared them to the weight maps obtained with the RF classifier.

Chapter 10 - Oil spill detection using optical remote sensing images and machine learning approaches (case study: Persian Gulf)

The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of deep learning. Based on our experiments, we drew three main conclusions: First, the OHS-1 hyperspectral images used in this study have high spatial resolution (10 m), which reduces the influence of mixed pixel effect and greatly improves the classification accuracy. Second, limited by the amount of sample data, Conv1D-based classifier does not need too many layers to achieve high classification accuracy. In addition, the size of the convolution kernel has a great influence on the classification accuracy. Finally, the accuracy of Conv1D (85.04%) is higher than that of RF model (80.61%). Especially for broadleaf species with similar spectral characteristics, such as Manchurian walnut and aspen, the accuracy of Conv1D-based classifier is significantly higher than RF classifier (87.15% and 71.77%, respectively). Thus, the Conv1D-based deep learning framework combined with hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects in the future.