Hierarchical Classification Research Articles

Morphological Classification of Galaxies Through Structural and Star Formation Parameters Using Machine Learning

Abstract We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0–S0a, Sa–Sb, Sbc–Scd, Sd–Irr) classes, using a combination of non-parametric (C, A, S, AS, Gini, M20, c5090), parametric (Sérsic index, n), geometric (axial ratio, BA), global colour (g − i, u − r, u − i), colour gradient (Δ(g − i)), and asymmetry gradient (ΔA9050) information, all estimated for a local galaxy sample (z < 0.15) compiled from the Sloan Digital Sky Survey (SDSS) imaging data. We train the XGBoost model and evaluate its performance through multiple standard metrics. Our findings reveal better performance when utilizing all fourteen parameters, achieving accuracies of 88% and 65% for the two-class and five-class classification tasks, respectively. In addition, we investigate a hierarchical classification approach for the five-class scenario, combining three XGBoost classifiers. We observe comparable performance to the “direct” five-class classification, with discrepancies of only up to 3%. Using SHAP (an advanced interpretation tool), we analyse how galaxy parameters impact the model’s classifications, providing valuable insights into the influence of these features on classification outcomes. Finally, we compare our results with previous studies and find them consistently aligned.

Hierarchical skin lesion image classification with prototypical decision tree

Traditional disease classification models often disregard the clinical significance of misclassifications and lack interpretability. To overcome these challenges, we propose a hierarchical prototypical decision tree (HPDT) for skin lesion classification. HPDT combines prototypical networks and decision trees, leveraging a class hierarchy to guide interpretable predictions from general to specific categories. By incorporating a hierarchy-based distance matrix, the model prioritizes less severe misclassifications while maintaining diagnostic accuracy. Evaluated on a dataset of 235,268 dermoscopic images across 65 conditions, HPDT outperforms flat classifiers and existing hierarchical methods in accuracy, error severity reduction, and interpretability. It also generalizes effectively to unseen classes. These results highlight the value of integrating clinical hierarchies into model design and training to improve diagnostic reliability and decision transparency, demonstrating HPDT’s potential for clinical decision support.

Indonesian Tourism Workers on Volcanoes and Geotourism's Colonial Origins: Making a Subaltern History Visible

Abstract Indonesia's volcanoes are places of recreation, aesthetic production, and scientific knowledge-gathering, as well as sites of pilgrimage, spirituality, and natural disasters for locals as well as international travellers. In this article, I focus on volcanoes as historic sites of labour to demonstrate the entanglement of colonial tourism and science with local forms of work and knowledge, and to reveal the origins of the porting and guiding work that takes place on Indonesia's volcanoes to this day. Using Tina Campt's method of “listening to images,” I show how colonial photographs, albeit partial sources, make modes of subaltern labour visible that written sources routinely minimised, restoring porters, guides, and what I call “camp domestics” to histories of service, science and geotourism in Indonesia. Recognising the homosocial setting of the colonial scientific expedition and the peculiar physical challenges of the volcano environment, I also examine the negotiation of Indonesian and European masculinities and their intersection with class and racial hierarchies on the volcano. The article thus reflects on how Javanese workers’ spatial and social mobility entailed the negotiation of opportunity as well as exploitation on tour.

“Come to Java”: Colonial Tourism and the Fragile Illusion of an “Island Paradise”

Abstract The proliferation of mountain resorts and the establishment of tourist itineraries in late colonial Indonesia reflected and reaffirmed colonial hierarchies of race, class, and gender. In the mild mountain climate, Europeans visited hotels reminiscent of European architectural styles, consumed European meals and enjoyed flora and fauna that reminded them of home. The location of these resorts was inspired by nearby natural, cultural, and ethnographic “wonders” that further served to emphasise colonial superiority. As spaces where pseudo-scientific concerns over race, civilisation, and the environment coalesced to legitimise colonial ideology, these mountain resorts were crucial elements in the development of a regional and national tourist network. The infrastructure needed to access these resorts and the identification of tourist destinations in the colonial era laid the foundation for many of Indonesia's contemporary tourist draws. Furthermore, thanks to a targeted international advertising campaign, these sites drew visitors both from within the Netherlands Indies as well from throughout colonial Asia and Oceania. Finally, these tourist networks traversed colonial (national) boundaries and played an important role in the articulation and experience of a shared colonial mindset. This history illustrates the extent to which contemporary tourism is profoundly shaped by the colonial discourse of power and difference.

Hierarchical classification of dynamic carbon emission factors based on improved support vector machine

Hierarchical classification of dynamic carbon emission factors based on improved support vector machine

Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues.

Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins. Cellular indexing of transcriptomes and epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell-type annotation requires a classifier that integrates multimodal data. Here, we describe multimodal classifier hierarchy (MMoCHi), a marker-based approach for accurate cell-type classification across multiple single-cell modalities that does not rely on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal subset markers. MMoCHi is designed for adaptability and can integrate annotation of cell types and developmental states across diverse lineages, samples, or modalities.

Reproducible and Generalizable Framework for Multi-class Hierarchical Classification Demonstrated by DNA Methylation-based Glioma Subtyping

Reproducible and Generalizable Framework for Multi-class Hierarchical Classification Demonstrated by DNA Methylation-based Glioma Subtyping

Two-tier classification framework for mapping grassland types using multisource earth observation data

ABSTRACT The dynamic changes in grassland types are crucial for conserving grassland biodiversity, conducting comprehensive health assessments, and gaining insights into ecosystem evolution. However, accurately mapping grassland types remains an ongoing challenge, especially over large areas. In response, we developed a hierarchical classification framework using random forest to tackle this task. This framework was structured into two tiers: land cover (LC) classification and grassland-type mapping, with each tier using specific features tailored to its respective objectives. In this framework, LC samples were automatically generated using existing LC products combined with vegetation indices and phenological features. We fused spectral information, phenological features, and habitat factors such as topographic indices, soil, and climate from multisource earth observation (EO) data to enhance grassland-type mapping performance. This framework successfully generated distribution maps of grassland types on the Qinghai Plateau for the years 1990–2020. Our findings revealed the following: (1) Using the two-tier classification framework and fusing multisource features, an ideal distribution map of grassland types was obtained, with a macro-average F1 score (F1) of 91% and an overall accuracy (OA) of 96%; (2) Compared to a one-shot classification framework, the two-tier classification framework achieved higher accuracy, with F1 and OA increasing by 11% and 7%, respectively; (3) Climate, topographical, soil, and phenological features assisted in distinguishing grassland types with similar spectral characteristics, especially for zonal grasslands. Adding these features increased F1 by 21%, 10%, 7%, and 3%, respectively, while OA increased by 9%, 5%, 5%, and 2%, respectively. Among these features, relative humidity, total precipitation from May to September, geographic coordinates, and elevation had the greatest effect on grassland type differentiation. June and July were the optimal phenological periods for mapping; (4) Over the 30-year period, the grasslands on the Qinghai Plateau showed an expanding trend, with extensive areas of alpine steppes transforming into alpine meadows. The results of this study provide the first elucidation of grassland-type changes across the Qinghai Plateau from 1990 to 2020. Moreover, they underscore the potential of hierarchical classification frameworks and the integration of multisource EO data in mapping grassland-type distribution.

Incremental classification of remote sensing images using feature pyramid and class hierarchy enhanced by label relationship graphs

Incremental classification of remote sensing images using feature pyramid and class hierarchy enhanced by label relationship graphs

HGBL: A Fine Granular Hierarchical Multi-Label Text Classification Model

Hierarchical multi-label text classification is vital for natural language processing (NLP). However, existing research rarely makes full use of the interaction between labels and text features that are crucial to hierarchical multi-label text classification. To address this issue, a novel model named hierarchy-guided BiLSTM guided contrastive learning classification (HGBL) is proposed, which successfully enhances the interaction between labels and text features by incorporating global context and embedding the idea of contrastive learning into this model. During modeling, Graphormer is adopted to model the dependencies between labels, and the bidirectional recurrent network (BiLSTM) is used to integrate global context including label features. Afterwards, the contrastive learning module embeds hierarchical awareness into the fine-tuned bidirectional encoder representations from transformers (BERT) by training the value of the loss. Experimental results on NYT, WOS and RCV1-V2 datasets show that HGBL exhibits significant competitive advantages compared with 19 competitors in terms of several indicators and can be used effectively for hierarchical multi-label text classification problems.

Voices of Resistance: Gender, Citizenship, and Rights in Miyah Poetry

The term Miyah has a specific meaning in Assam. Unlike in other parts of India, where it is used to address a gentleman in an honourable way, in Assam it is a pejorative term that refers to the community of Assamese Muslims of Bengali origin. This community is now reclaiming this identity through various forms of creative expression including poetry. The perpetual “other” in Assamese nationalist assertions, the Miyah until recently was accorded very little agency in the popular imagination. Miyah poetry emerged as a powerful mode of resisting injustice and suffering during the preparation of the National Register of Citizens in Assam (NRC), a complex and tedious bureaucratic exercise that could render a huge section of the population “undocumented migrants.” This is a genre of poetry that is resisting two kinds of power relationships – the hegemony of the Assamese language and the class hierarchy associated with the term Miyah in everyday usage. I argue that there is an explicit gender dimension of the word Miyah which has remained largely unexplored. The question of citizenship has different implications for men and women within the Miyah community, and it merits our attention to explore this difference through a feminist reading of Miyah poetry.

“Gender Ideology” as a Strategic Enemy of the Radical Right in South America: The Cases of Bolsonaro and Milei

ABSTRACT This paper analyses the construction of “gender ideology” (GI) as a strategic enemy of the radical right in a context of radicalisation of neoliberalism in South America. Drawing on studies on the radical right and anti-gender movements, the paper suggests that the growing influence of feminist and LGBTQI+ struggles have been contested by the cultural backlash of conservative mobilisations and of the radical right. In that framework, the paper shows the confluence between religious and political anti-gender activism, recovering epistemic, moral, ideological and geopolitical dimensions that explain the erection of GI as a strategic and absolute enemy of the radical right. Then, the paper discusses anti-gender movements as a reaction against advances in sexual and reproductive rights and analyses the similarities and differences between discourses, policies and contexts of Milei and Bolsonaro. Hence, the article discusses how GI has enabled a right-wing intersectionality, which defends racial, gendered and class hierarchies against the movements that challenge them. It concludes by posing authoritarian neoliberalism and radical right-wing populism as a threat to liberal democracy and the rule of law.

30 m 5-yearly land cover maps of Qilian Mountain Area (QMA_LC30) from 1990 to 2020

The Qilian Mountain Area (QMA) serves as a crucial ecological barrier and strategic water conservation zone in China. Recent years have seen heightened social attention to environmental issues within the QMA, underscoring the need for accurate and continuous land cover maps to support ecological monitoring, analysis, and forecasting. This paper presents the QMA_LC30 dataset, which includes 9 land cover categories and spans the period from 1990 to 2020, with updates every 5 years. The dataset primarily utilizes 30 m Landsat series data and features: 1) High precision, achieved through a geographical division and hierarchical classification decision tree approach, complemented by visual interpretation. 2) Robust consistency, ensured by a change detection method based on a benchmark map. The QMA_LC30 dataset undergoes rigorous accuracy validation, achieving an overall accuracy of over 0.92 for all 7 periods of land cover maps. Compared to GlobeLand30, ESA WorldCover, ESRI 2020 Land Cover, FROM_GLC30, and GLC_FCS30, QMA_LC30 demonstrates the highest consistency with remote sensing images.

ANALYSIS OF THE INFLUENCE OF EXTERNAL TEMPERATURE ON THE SPECIFIC GAS CONSUMPTION OF HEAT SOURCES

The article examines the impact of outdoor temperature on the specific consumption of gas from heat sources, taking into account tariffs for district heating in the regions of Ukraine. The study is aimed at identifying patterns between external climatic conditions and gas consumption for heating, which allows optimizing energy consumption and increasing the efficiency of energy use. Data on district heating tariffs, population and area of Ukrainian regions were analyzed. The cluster analysis and hierarchical classification of regions were performed, which allowed us to identify groups with similar characteristics of gas consumption and heating tariffs. The ratio of population to area of regions and its impact on gas consumption and tariff policy is investigated.

An Assessment of the Market Performance of Commercial and Residential Property Investments in Lekki, Lagos, Nigeria

The housing deficit issue in Lagos is creating investment opportunities for prospective investors to profit from in prime zones. Still, some investments are executed by investors with the assumption that their location or remitted rents validate their investment soundness. The purpose of this research was to assess the market performance of commercial and residential property investments in Lekki, Lagos, Nigeria with the target of directing sound investment decision-making amongst investors. Its objectives include; the examination of the drivers of demand for the two classes of property investment, the determination of the user demand hierarchy of the investment classes, the observation of factors that determine the market performance of the investment classes and the measurement of market performance of the investment classes in the study area. The Survey Research Design was adopted, while the sample selection was via Krejcie and Morgan’s technique. A total of 108 questionnaires were distributed across registered professional firms within Lekki. A total of 78 questionnaires were retrieved and a 72.23% response rate was achieved. Holding Period Return (HPR), Coefficient of Variation, Standard Deviation and Geometric Mean Return were employed in data analysis. It was discovered that the major driver of demand for commercial property investment in each zone of the Lekki axis is its business hub status, while the drivers for its residential counterpart are; Phase 1 (Prestige), Ikate (State of the economy) and Ikota (Level of infrastructure). The findings reveal that within Lekki, Ikota (11.4 & 9.63) had a better market performance of residential and commercial property investments compared to Ikate (10.87 & 9.16) and Phase 1 (10.08 & 9.04) in terms of their average holding period return and time weighted rate of return. The study recommended the consideration of the Ikota and Ikate axis for future residential and commercial investments by potential investors.

Gallbladder cancer detection via ultrasound image analysis: An end‐to‐end hierarchical feature‐fused model

AbstractGallbladder cancer is a fatal disease, and its early diagnosis can significantly impact patient treatment. Ultrasound imaging is often the initial diagnostic test for gallbladder cancer, making the enhancement of cancer detection accuracy from these images crucial. Despite the promising results of artificial intelligence techniques in disease diagnosis, their black‐box nature hinders the reliability of their results and their practical application. Therefore, it is essential not to rely solely on a single model's output and to further investigate for more reliable outcomes. This study presents a step‐by‐step structural investigation of forming an end‐to‐end model, a conjunction of two convolutional neural network based methods, for detecting gallbladder conditions. The final model, leveraging feature fusions and hierarchical classification, achieved a high accuracy of 92.62% for detecting normal, benign, and malignant gallbladders. It also achieved a remarkable accuracy of 98.36% for classifying normal and non‐normal instances and 92.22% for classifying benign and malignant cases. Finally, comprehensive post‐processing investigations, including cross‐validation, temperature scaling, and uncertainty estimation, along with error analysis, are conducted to gain more insights into the model's output. Among these insights, the model demonstrated resilience of its results to active dropout and augmentation at the inference phase. Furthermore, when applied with test‐time data augmentation, uncertainty estimation methods have better distinguishability between the uncertainties of correctly and incorrectly classified instances, which provides additional information about the model's output. The source code of experiments conducted in this study is available at https://github.com/SaraDadjouy/GBCRet.

Digitalisation as a Challenge for Smart Villages: The Case of Poland

Rural areas face several development challenges. Some lead to rural decline—such as depopulation—and others are intended to counteract this by revitalising the countryside—such as digitalisation. These two processes are on the agenda of the European Union’s new rural development concept, smart villages. The study aims to identify the spatial differentiation of the digitalisation challenge in Polish rural areas. An attempt was made to operationalise two aspects of this challenge—access to fast Internet and digital competence. The subject of the analysis covered rural areas in Poland at the municipal level. The temporal scope of the study is defined by two approaches—static and dynamic. The first aims to show the state of the ‘here and now,’ while the second aims to identify the change intensity. Methods of multivariate comparative analysis were used, resulting in hierarchical classifications of municipalities. The results show significant regional differentiation. Municipalities with predominantly ageing populations face greater difficulties in adopting new digital technologies. Overcoming these disparities will be key to improving the quality of life and resilience of rural communities. The study results provide evidence to justify the need for place-based targeted digital investments under the smart villages programmes.

A Pathological Diagnosis Method for Fever of Unknown Origin Based on Multipath Hierarchical Classification: Model Design and Validation.

Fever of unknown origin (FUO) is a significant challenge for the medical community due to its association with a wide range of diseases, the complexity of diagnosis, and the likelihood of misdiagnosis. Machine learning can extract valuable information from the extensive data of patient indicators, aiding doctors in diagnosing the underlying cause of FUO. The study aims to design a multipath hierarchical classification algorithm to diagnose FUO due to the hierarchical structure of the etiology of FUO. In addition, to improve the diagnostic performance of the model, a mechanism for feature selection is added to the model. The case data of patients with FUO admitted to the First Affiliated Hospital of Xi'an Jiaotong University between 2011 and 2020 in China were used as the dataset for model training and validation. The hierarchical structure tree was then characterized according to etiology. The structure included 3 layers, with the top layer representing the FUO, the middle layer dividing the FUO into 5 categories of etiology (bacterial infection, viral infection, other infection, autoimmune diseases, and other noninfection), and the last layer further refining them to 16 etiologies. Finally, ablation experiments were set to determine the optimal structure of the proposed method, and comparison experiments were to verify the diagnostic performance. According to ablation experiments, the model achieved the best performance with an accuracy of 76.08% when the number of middle paths was 3%, and 25% of the features were selected. According to comparison experiments, the proposed model outperformed the comparison methods, both from the perspective of feature selection methods and hierarchical classification methods. Specifically, brucellosis had an accuracy of 100%, and liver abscess, viral infection, and lymphoma all had an accuracy of more than 80%. In this study, a novel multipath feature selection and hierarchical classification model was designed for the diagnosis of FUO and was adequately evaluated quantitatively. Despite some limitations, this model enriches the exploration of FUO in machine learning and assists physicians in their work.

Rail Flaw B-Scan Image Analysis Using a Hierarchical Classification Model

AbstractAs railway traffic volumes and train speeds increase, rail maintenance is becoming more crucial to prevent catastrophic failures. This study aimed to develop an artificial intelligence (AI)-based solution for automatic rail flaw detection using ultrasound sensors to overcome the limitations of traditional inspection methods. Ultrasound sensors are well-suited for identifying structural abnormalities in rails. However, conventional inspection techniques like rail-walking are time-consuming and rely on human expertise, risking detection errors. To address this, a hierarchical classification model was proposed integrating ultrasound B-scan images and machine learning. It involved a two-stage approach—model A for fuzzy classification followed by Model EfficientNet-B7 was identified as the most effective architecture for both models through network comparisons. Experimental results demonstrated the model's ability to accurately detect rail flaws, achieving 88.56% accuracy. It could analyze a single ultrasound image sheet within 0.45 s. An AI-based solution using ultrasound sensors and hierarchical classification shows promise for automated, rapid, and reliable rail flaw detection to support safer railway infrastructure inspection and maintenance activities.

A retrospective multi-site examination of chronic kidney disease using longitudinal laboratory results and metadata to identify clinical and financial risk

BackgroundA retrospective observational study was conducted at 3 health care organizations to identify clinical gaps in care for patients with stage 3 or 4 chronic kidney disease (CKD), and financial opportunity from U.S. risk adjustment payment systems. Lack of evaluation for CKD in patients with diabetes was also assessed.MethodsOutpatient longitudinal laboratory results and patient metadata available in the electronic medical record, laboratory information system, and/or laboratory billing or facility claims data for the calendar year 2021 were evaluated. Laboratory results were compared to billing data (ICD-10 codes) and risk adjustment scores including Hierarchical Condition Categories (HCC) to determine if laboratory-identified CKD was coded as a disease condition in the electronic medical record. Adults 18 to 75 years of age were included; inpatient laboratory results and pregnant individuals were excluded.ResultsAt the 3 institutions, 12,478 of 16,063 (78%), 487 of 1511 (32%) and 19,433 of 29,277 (66%) of patients with laboratory evidence of stage 3 or 4 CKD did not have a corresponding ICD-10 or HCC code for CKD in the electronic medical record. For patients at the 3 institutions with diabetes on the basis of an HbA1c value of ≥ 6.5%, 34,384 of 58,278 (59%), 2274 of 2740 (83%) and 40,378 of 52,440 (77%) had not undergone guideline-recommended laboratory testing for CKD during the same 12 months. Using publicly available data for calendar year 2021, an estimated 3246 of 32,398 patients (9.9%) at the 3 institutions with undocumented CKD stages 3–4 would be enrolled in Medicare Advantage or Affordable Care Act Marketplace programs. The imputed lost reimbursement under risk-adjusted payment systems for under-documentation of CKD in this subset of patients was $2.85 M for the three institutions combined, representing lost opportunity for both identification and proactive clinical management of these patients, and financial recovery for the costs of providing that care.ConclusionsClinical laboratories can provide value beyond routine diagnostics, helping to close gaps in care for identification and management of CKD, stratifying subgroups of patients to identify risk, and capturing missed reimbursement through risk adjustment factors.