Categorical Data Sets Research Articles

Financial Modelling System Using Deep Neural Networks (DNNs) for Financial Risk Assessments

ABSTRACTThe FOREX market assessment is a big challenge for investors and global risk managers. However, the present study uses daily multicurrency exchange rate returns data from 2007 to 2022 to estimate the learning returns performance of the proposed model to find a safe‐haven currency for optimal investment strategy. The categorical returns are classified into good returns (GRs), bad returns (BRs) and no returns (NRs). Therefore, the present study needs to use a one‐hot‐encoding function to convert a categorical dataset into a numeric format with TensorFlow. The present study proposes a deep neural network‐based multilayer perceptron (DNN‐based MLP) with a backpropagation algorithm to estimate the learning returns performance of the proposed model to find a safe‐haven currency for optimal investment strategy. The findings showed that currency exchange rate return 2 (CERR2) is relatively a safe‐haven currency than currency exchange rate return 1 (CERR1) and currency exchange rate return 3 (CERR3). Moreover, the findings also showed that the proposed model gives optimal learning return performance. This study may assist FOREX investors to modify their investment strategies under shed light of findings of the study. In addition, the findings of the present study may also support global risk managers to revisit their hedging strategies.

Toward parametric Bayesian adaptive procedures for multi-frequency categorical loudness scaling.

A series of Bayesian adaptive procedures to estimate loudness growth across a wide frequency range from individual listeners was developed, and these procedures were compared. Simulation experiments were conducted based on multinomial psychometric functions for categorical loudness scaling across ten test frequencies estimated from 61 listeners with normal hearing and 87 listeners with sensorineural hearing loss. Adaptive procedures that optimized the stimulus selection based on the interim estimates of two types of category-boundary models were tested. The first type of model was a phenomenological model of category boundaries adopted from previous research studies, while the other type was a data-driven model derived from a previously collected set of categorical loudness scaling data. An adaptive procedure without Bayesian active learning was also implemented. Results showed that all adaptive procedures provided convergent estimates of the loudness category boundaries and equal-loudness contours between 250 and 8000 Hz. Performing post hoc model fitting, using the data-driven model, on the collected data led to satisfactory accuracies, such that all adaptive procedures tested in the current study, independent of modeling approach and stimulus-selection rules, were able to provide estimates of the equal-loudness-level contours between 20 and 100 phons with root-mean-square errors typically under 6 dB after 100 trials.

EDMD: An Entropy based Dissimilarity measure to cluster Mixed-categorical Data

The effectiveness of clustering techniques is significantly influenced by proximity measures irrespective of type of data and categorical data is no exception. Most of the existing proximity measures for categorical data assume that all attributes contribute equally to the distance measurement which is not true. Usually, frequency or probability-based approaches are better equipped in principle to counter this issue by appropriately weighting the attributes based on the intra-attribute statistical information. However, owing to the qualitative nature of categorical features, the intra-attribute disorder is not captured effectively by the popularly used continuum form of entropy known as Shannon or information entropy. If the categorical data contains ordinal features, then the problem multiplies because the existing measures treat all attributes as nominal. To address these issues, we propose a new Entropy-based Dissimilarity measure for Mixed categorical Data (EDMD) composed of both nominal and ordinal attributes. EDMD treats both nominal and ordinal attributes separately to capture the intrinsic information from the values of two different attribute types. We apply Boltzmann’s definition of entropy, which is based on the principle of counting microstates, to exploit the intra-attribute statistical information of nominal attributes while preserving the order relationships among ordinal values in distance formulation. Additionally, the statistical significance of different attributes of the data towards dissimilarity computation is taken care of through attribute weighting. The proposed measure is free from any user-defined or domain-specific parameters and there is no prior assumption about the distribution of the data sets. Experimental results demonstrate the efficacy of EDMD in terms of cluster quality, accuracy, cluster discrimination ability, and execution time to handle mixed categorical data sets of different characteristics.

Finding mixed memberships in categorical data

Latent class analysis, a fundamental problem in categorical data analysis, often encounters overlapping latent classes that introduce further challenges. This paper presents a solution to this problem by focusing on finding latent mixed memberships of subjects in categorical data with polytomous responses. We employ the Grade of Membership (GoM) model, which assigns each subject a membership score in each latent class. To address this, we propose two efficient spectral algorithms for estimating these mixed memberships and other GoM parameters. Our algorithms are based on the singular value decomposition of a regularized Laplacian matrix. We establish their convergence rates under a mild condition on data sparsity. Additionally, we introduce a metric to evaluate the quality of estimated mixed memberships for real-world categorical data and determine the optimal number of latent classes based on this metric. Finally, we demonstrate the practicality of our methods through experiments on both computer-generated and real-world categorical datasets.

Predicting Cryptocurrency Returns Using Classification and Regression Machine Learning Model

People are starting to see the cryptocurrency market as a viable source of income and investment, similar to the stock market, as the concept of cryptocurrencies continues to gain popularity. Predicting Bitcoin returns is related to financial machine learning, which uses time series to forecast price variance. This study starts with the daily close price of Bitcoin for its initial dataset. The price is transformed into percentages and binary classes, which categorize into “Up” and “Down”, after which a time series is applied to produce two datasets: a categorical dataset for classification and a numerical dataset for regression. For classification that represents a Binary classification in asset-price forecasting, k-fold cross-validation is applied to ensure that the best classifiers are selected for testing and analysis. Most of the regression analysis was based on visualization, which displayed the predicted prices by each regressor in front of the original values and helped analyse the models’ results more accurately. The outcomes of this study were achieved by anticipating bitcoin returns using classification and regression machine learning models, despite the approaches’ low accuracy and significant precision rate to the “Up” class. At this stage, with a significant limitation regarding the dataset and a lack of other indicators, a model capable of predicting future variations is considered a beneficial addition for many trading tools or even for crypto market analysts.

Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces.

Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity. Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification. Achievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications.

Processing and optimized learning for improved classification of categorical plant disease datasets

PURPOSE: Crop diseases can cause significant reductions in yield, subsequently impacting a country’s economy. The current research is concentrated on detecting diseases in three specific crops – tomatoes, soybeans, and mushrooms, using a real-time dataset collected for tomatoes and two publicly accessible datasets for the other crops. The primary emphasis is on employing datasets with exclusively categorical attributes, which poses a notable challenge to the research community. METHODS: After applying label encoding to the attributes, the datasets undergo four distinct preprocessing techniques to address missing values. Following this, the SMOTE-N technique is employed to tackle class imbalance. Subsequently, the pre-processed datasets are subjected to classification using three ensemble methods: bagging, boosting, and voting. To further refine the classification process, the metaheuristic Ant Lion Optimizer (ALO) is utilized for hyper-parameter tuning. RESULTS: This comprehensive approach results in the evaluation of twelve distinct models. The top two performers are then subjected to further validation using ten standard categorical datasets. The findings demonstrate that the hybrid model II-SN-OXGB, surpasses all other models as well as the current state-of-the-art in terms of classification accuracy across all thirteen categorical datasets. II utilizes the Random Forest classifier to iteratively impute missing feature values, employing a nearest features strategy. Meanwhile, SMOTE-N (SN) serves as an oversampling technique particularly for categorical attributes, again utilizing nearest neighbors. Optimized (using ALO) Xtreme Gradient Boosting OXGB, sequentially trains multiple decision trees, with each tree correcting errors from its predecessor. CONCLUSION: Consequently, the model II-SN-OXGB emerges as the optimal choice for addressing classification challenges in categorical datasets. Applying the II-SN-OXGB model to crop datasets can significantly enhance disease detection which in turn, enables the farmers to take timely and appropriate measures to prevent yield losses and mitigate the economic impact of crop diseases.

A new support vector machine for categorical features

Support vector machine (SVM) was originally developed to solve binary classification problems for objects with only continuous features, and it often outperforms other classifiers. However, we often encounter datasets with mixed-type features or categorical features only. This study proposes an efficient SVM for dealing with such datasets. The proposed SVM uses a subset of categorical features and it performs well in most cases, including imbalanced and/or high-dimensional categorical datasets. In particular, it is more efficient than existing SVMs in high-dimensional categorical datasets. To validate its performance, it is applied to simulated datasets and various benchmark datasets, and it is also compared to existing SVMs for categorical features.

A hybrid intelligent model based on logistic regression and fuzzy multiple-attribute decision-making for credit evaluation

. One of the crucial issues in data mining is to select an appropriate classification algorithm. Due to it usually involves many criteria, the duty of algorithm selection can be widely described as multiple-attribute decision-making (MADM) problems, including credit risk evaluation. Many different MADM approaches select classifiers based on different perspectives, and hence they might generate diverse classifiers' rankings. This paper aims to propose a hybrid intelligent model to overcome credit risk assessment problems based on logistic regression and the fuzzy MADM method. Firstly, the Ordinal Priority Approach (OPA) method evaluates attributes involved in credit risk problems by considering professional assessments of a decision-maker and calculates a weight for each criterion. Secondly, all categorical data converted into triangular-fuzzy numbers (TFNs) and numerical data are evaluated using the MADM instrument to obtain an optimal solution dataset and logistic regression to calculate the probabilities of the optimal dataset. In this experimental study, three existing classification techniques and the proposed intelligent model evaluate three banking credit datasets with a different number of criteria under numerical and categorical data types. The prediction accuracy results generated by the proposed model are compared with the three existing classification methods. The results exhibit that there are slight differences between the three datasets. The experimental results demonstrate the proposed intelligent model has superiority in classifying the credit loan recipients especially for categorical datasets.

Comparison of implant-supported rehabilitation in smokers and non-smokers with conventional and short tuberosity implants: an evaluation of prosthetic, peri-implant, and cytokine profiles.

This study aimed to assess periodontal parameters, radiographic (CBL), and prosthetic parameters along with levels of matrix metalloproteinase-9 (MMP-9) and interleukin-1β (IL-1β) in smokers undergoing rehabilitation using conventional implants and short tuberosity implants (STIs). The duration of the study was six months. A structured questionnaire was made to be filled out by all participants. The participants were included in the study based on predefined inclusion and exclusion criteria for smokers and non-smokers with STIs. Peri-implant parameters were assessed based on peri-implant plaque index (PIPI), bleeding on probing (BoP), and peri-implant periodontal depth (PIPD) ≥4 mm. Collection of peri-implant crevicular fluid (PICF) and measurement of MMP-9 and IL-1β was performed using ELISA. Data related to peri-implant clinical and radiographic parameters were reported in mean and percentages. Pearson Chi-square test was employed for categorical data sets, whereas the Kruskal-Wallis test was used for the comparison of means between groups. Bonferroni post hoc adjustment test was applied for multiple comparisons. Differences were found to be significant p<0.01 RESULTS: Among the four groups, one hundred participants were included. The mean age of participants in groups 1 (44±4.5 yrs) and 3 (44±2.1 yrs) showed no significant difference from participants in groups 2 (42±3.8 yrs) and 4 (43±3.5 yrs). The duration of the smoking habit in cigarette smokers with STIs was 22.7±1.4 yrs, and cigarette smokers with conventional implants were 23.8±1.9 yrs with a daily frequency of 11.2±2.5 in group 1 and 11.33±2.1 in group 3. The means for PIPI and PIPD were found to be significantly worse in cigarette smokers with STIs (PIPI 62.4±5.9; PIPD 5.3±2.1) and conventional implants (PIPI 63.3±6.1; PIPD 5.5±1.9) compared to non-smokers with STIs (PIPI 29.2±3.6; PIPD 3.1±0.1) and conventional implants (PIPI 28.1±3.4; PIPD 3.2±0.3). BoP was significantly higher in non-smokers compared to smokers with STIs (smokers 24.2±8.3; non-smokers 36.5±21.2) and conventional implants (smokers 21.6±7.4; non-smokers 38.4±24.1) (p<0.01). The level of IL-1β (pg/ml) and the level of MMP-9 (ng/ml) were found to be significantly higher in cigarette smokers with STIs and conventional implants in comparison to non-smokers (p<0.01). Periodontal (PIPI, PIPD, and BoP) along with radiographic (CBL) and prosthetic parameters were compromised in smokers compared to non-smokers. Patients with conventional implants and STI showed comparable clinical, radiographic, and prosthetic parameters among smokers. Utilization of dental services along with cessation programs should be encouraged for smokers.

The Ideal Orthopaedic Surgeon: Comparing Patient Preferences of Surgeon Attributes to Notions Held by Orthopaedic Postgraduates.

Stereotypes have been a barrier to providing patients a diverse orthopaedic workforce. Our goal was to identify stereotypes and disparities among doctors and their patients regarding the attributes that should determine a competent orthopaedic surgeon. A cross-sectional descriptive multicenter study was conducted in India. Tailored questionnaires were administered to patients and orthopaedic postgraduates to determine the attributes they believe patients prefer in their orthopaedic surgeon. Likert data and data on preferred sex of the surgeon were analyzed as categorical data sets using frequency statistics. Participants were asked to rank surgeon attributes and analysis was based on frequency of an item among top 5 surgeon attributes. 304 patients and 91 orthopaedic postgraduates participated in the study. 70.4% and 73% of patients and 27.5% and 29.6% of postgraduates preferred an orthopaedic surgeon with greater physical strength as an outpatient consultant or operating surgeon respectively. 81% of patients had no preference of the sex of their doctor. 56% of postgraduates felt patients would prefer a male operating surgeon, none felt their patient would prefer female orthopaedic surgeon. 92.3% of the female postgraduates felt patients would prefer a male orthopaedic surgeon. Patients most often ranked years of experience, surgical outcomes, time spent with patients, reputation, and physical strength in their top 5 surgeon attributes and sex, religion, and community were given least importance. Diversity among the orthopaedic workforce is necessary to optimize patient care. It is our collective responsibility to educate our patients and trainees and redress the misconceptions and stereotypes that plague our profession. The online version contains supplementary material available at 10.1007/s43465-023-00988-2.

Machine learning-based heat deflection temperature prediction and effect analysis in polypropylene composites using catboost and shapley additive explanations

Among the various physical properties of polypropylene composites (PPCs), heat deflection temperature (HDT) during PPC production is significant because it is directly related to the mechanical behavior of such products. However, it is difficult to predict and analyze the HDT of PPCs owing to the absence of a mathematical or theoretical model. Moreover, categorical data which has different physical properties despite using same substances made predictions highly difficult in PPCs. Here, this study proposed an indicator to analyze the categorical data and Catboost-based model for HDT prediction considering the categorical data. First, the categorization and minimum-based values (MBVs), a dimensionless factor used to calculate HDT differences in the categorical dataset, are applied to detect and split categorical data in a PPC dataset. Second, a case study was conducted on the dataset using three algorithms to compare the proposed model with other traditional machine-learning approaches. As a result, the proposed model provides the highest prediction performance of R2=0.8965 and 0.9801, for the total test dataset and the categorical dataset, respectively. In addition, the effect analysis of substances on HDT was conducted to get some prospective substances for the required HDT using Shapley Additive Explanations (SHAP). Thus, the results of this study can provide a guidance for selecting prospective substances using SHAP result for the target HDT and adjusting the substance ratio using the proposed model. It is expected that this framework has the potential for being applied to the other blending processes to produce products with the required properties.

Are highly accurate models of agency in bone breaking the result of misuse of machine learning methods?

Machine learning (ML) methods constitute a major advance in analytical archaeology and taphonomy. Their adoption by anthropologists, usually with limited training in data analysis, has recently raised some concerns about their correct use. Here, we test some of the main objections made regarding the standard use of ML algorithms on categorical taphonomic data sets, more specifically on the issue of agency in bone breaking analysis. We show that the correct use of ML generates models that are accurate because of their data properties, and not because of methodological biasing during training and testing. We also show alternative tools, like ensemble learning, that allows to increase the confidence of accurate models used for differentiating agency in bone breaking. The present work shows how experimental hammerstone-broken long bones and carnivore-broken long bones can be correctly classified with a moderate (>70 %) to high degree (>80 %) of accuracy by several ML methods. The results reinforce the confidence with which ML is being applied in Anthropology, and underscore the great improvement of their use for the analytical study of bone breaking in taphonomy.

Towards Efficient and Domain-Agnostic Evasion Attack with High-Dimensional Categorical Inputs

Our work targets at searching feasible adversarial perturbation to attack a classifier with high-dimensional categorical inputs in a domain-agnostic setting. This is intrinsically a NP-hard knapsack problem where the exploration space becomes explosively larger as the feature dimension increases. Without the help of domain knowledge, solving this problem via heuristic method, such as Branch-and-Bound, suffers from exponential complexity, yet can bring arbitrarily bad attack results. We address the challenge via the lens of multi-armed bandit based combinatorial search. Our proposed method, namely FEAT, treats modifying each categorical feature as pulling an arm in multi-armed bandit programming. Our objective is to achieve highly efficient and effective attack using an Orthogonal Matching Pursuit (OMP)-enhanced Upper Confidence Bound (UCB) exploration strategy. Our theoretical analysis bounding the regret gap of FEAT guarantees its practical attack performance. In empirical analysis, we compare FEAT with other state-of-the-art domain-agnostic attack methods over various real-world categorical data sets of different applications. Substantial experimental observations confirm the expected efficiency and attack effectiveness of FEAT applied in different application scenarios. Our work further hints the applicability of FEAT for assessing the adversarial vulnerability of classification systems with high-dimensional categorical inputs.

Novel Biomarker Evaluation in Peri-Implant Crevicular Fluid as Disease Indicators for Peri-Implant Health

Objective: To assess various inflammatory biomarkers CCL-20, BAF, RANK-L, IL-23, and osteoprotegerin from PICF along with clinical parameters in participants with peri-implant mucositis and peri-implantitis. Material and Methods: Participants were gathered i.e., 30 with periimplantitis, 32 with peri-implant mucositis, and 32 healthy. Peri-implant parameters PIPD, BoP, and PIPI were measured in all participants. PICF samples were collected to assess the level of biomarkers CCL-20; BAF; IL-23; RANK-L, and OPG. For periodontal parameters, ranges and means were measured. Kruskal Wallis test was used for comparison between groups. For categorical data sets, the Pearson Chi-Square test was applied. The Bonferroni test was used for multiple comparisons. Results: Peri-implant parameter BoP in peri-implant mucositis and peri-implantitis was significantly higher compared to controls (p &lt; 0.05). PIPD with healthy peri-implant conditions was, significantly less compared to peri-implantitis, and peri-implant mucositis (p&lt;0.05). PIPI demonstrated no significant difference throughout different peri-implant conditions. CCL-20 ng/mL in patients with peri-implant mucositis was found to be significantly higher (p &lt; 0.05). BAF levels in peri-implantitis and peri-implant mucositis were comparable. The concentration of IL-23 ng/mL was found to be significantly lower in healthy controls (p &lt; 0.05). Conclusion: Inflammatory biomarkers showed high levels of PICF in peri-implant disease patients compared to healthy controls. Individuals with peri-implant conditions experience the poor peri-implant probing depth and bleeding on probing.

COMPARISON OF SIMPLE MISSING DATA IMPUTATION TECHNIQUES FOR NUMERICAL AND CATEGORICAL DATASETS

Almost every dataset has missing data. The common reasons are sensor error, equipment malfunction, human error, or translation loss. We study the efficacy of statistical (mean, median, mode) and machine learning based (k-nearest neighbors) imputation methods in accurately imputing missing data in numerical datasets with data missing not at random (MNAR) and data missing completely at random (MCAR) as well as categorical datasets. Imputed datasets are used to make prediction on the test set and Mean squared error (MSE) in prediction is used as the measure of performance of the imputation. Mean absolute difference between the original and imputed data is also observed. When the data is MCAR, kNN imputation results in lowest MSE for all datasets, making it the most accurate method. When less than 20% of data is missing, mean and median imputations are effective in regression problems. kNN imputation is better at 20% missingness and significantly better when 50% or more data is missing. For the kNN method, k = 5 gives better results than k=3 but k=10 gives similar results to k=5. For MNAR datasets, statistical methods result in similar or lower MSE compared to kNN imputation when less than 25% of instances have a missing feature. For higher missing levels, kNN imputation is superior. Given enough data points without missing features, deleting the instances with missing data may be a better choice at lower missingness levels. For categorical data imputation, kNN and Mode imputation are both effective.

A benchmark dataset and approach for fine-grained visual categorization in complex scenes

With the vast development of deep learning, many deep learning-based approaches have demonstrated their outstanding performance on the task of fine-grained visual categorization (FGVC). However, existing fine-grained datasets mainly focus on simple images (i.e., objects tend to occupy a significantly larger portion of the image and appear in a relatively clear background). This seriously restricts the application of FGVC in real-world scenarios. In this paper, we construct a fine-grained dataset named AIBD-Cars, which contains 28,471 car images with complex backgrounds belonging to 196 fine-grained classes. Furthermore, we propose a Location-Aware Channel-Spatial Attention Network (LCSANet), which considers both locating object regions and mining discriminative information to achieve better fine-grained visual categorization in complex scenes. We evaluate popular fine-grained visual categorization algorithms to build a benchmark. Extensive experiments show that our proposed method achieves a new state of the art on AIBD-Cars and FGVC Aircraft, and competitive results on CUB-200-2011 and Stanford Cars.

Graph Enhanced Fuzzy Clustering for Categorical Data Using a Bayesian Dissimilarity Measure

Categorical data are widely available in many real-world applications, and to discover valuable patterns in such data by clustering is of great importance. However, the lack of a decent quantitative relationship among categorical values makes traditional clustering approaches, which are usually developed for numerical data, perform poorly on categorical datasets. To solve this problem and boost the performance of clustering for categorical data, we propose a novel fuzzy clustering model in this article. At first, by approximating the maximum a posteriori (MAP) estimation of a discrete distribution of data partition, a new fuzzy clustering objective function is designed for categorical data. The Bayesian dissimilarity measure is formulated in this objective to tackle the subtle relationships between categorical values efficiently. Then, to further enhance the performance of clustering, a novel Kullback–Leibler divergence-based graph regularization is integrated into the clustering objective to exploit the prior knowledge on datasets, for example, the information about correlations of data points. The proposed model is solved by the alternative optimization and the experimental results on the synthetic and real-world datasets show that it outperforms the classical and relevant state-of-the-art algorithms. We also present the parameter analysis of our approach, and conduct a comprehensive study on the effectiveness of the Bayesian dissimilarity measure and the KL divergence-based graph regularization.

A generalized multi-aspect distance metric for mixed-type data clustering

Distance calculation is straightforward when working with pure categorical or pure numerical data sets. Defining a unified distance to improve the clustering performance for a mixed data set composed of nominal, ordinal, and numerical attributes is very challenging due to the attributes’ different natures. In this study, we proposed a new measure of distance for a mixed-type data set that regards inter-attribute information and intra-attribute information depending on the type of attributes. In this regard, entropy and Jensen–Shannon divergence concepts were used to exploit the inter-attribute information of categorical-categorical and categorical-numerical attributes, respectively. Also, a modified version of Mahalanobis distance was proposed to consider the intra- and inter-attribute information of numerical attributes. We also introduced a unified framework based on mutual information to control attributes’ contribution to distance measurement. The proposed distance in conjunction with spectral clustering was extensively evaluated concerning various categorical, numerical, and mixed-type benchmark data sets, and the results demonstrated the efficacy of the proposed method.

How Is the American Foreign Policy Establishment Structured? A Multiple Correspondence Analysis of the US China Field

AbstractChallenging visions of the US national security “Establishment” as either hamstrung by ideological homogeneity bordering on groupthink, or riven by irreconcilable party political divisions, we deploy multiple correspondence analysis (MCA) to uncover empirically the structure of the China segment of the US national security community. MCA—a form of geometric data analysis—finds latent structures in categorical datasets. Using an original dataset of organizations with a strong presence on China issues, we show that the China organizational field is far from homogenous, but is split less between left and right than over degree of political engagement, separating older think tanks and cultural organizations from business-focused research and consulting firms, and academic/research institutions outside from politically oriented groups located primarily within the Beltway. We explore our findings using the Trump and Obama administrations’ choice of locations to give speeches and remarks on US–China relations.