Hierarchical Classification Approach Research Articles

Mixture of deep networks for facial age estimation

In this paper, our objective is to simultaneously explore the learning of ordinal relationships among age labels and address the challenge of heterogeneous data resulting from the non-stationary aging process through an advanced mixture model of deep networks. Drawing upon the pivotal insight that the non-stationary aging process can be decomposed into a series of stationary subprocesses, we employ a divide-and-conquer strategy. This involves initially partitioning the age spectrum into multiple groups and subsequently training a specialized deep network, referred to as an “expert”, for each distinct group. These experts are not functionally independent; instead, they are interconnected through specialized model designs and a joint training mechanism that consolidates them into a unified system. As a result, the learning of ordinal relationships is consistently maintained by solving the age-related tasks across the entire age label set. The final age estimation is accomplished through a hierarchical classification approach, leveraging the collective outputs from all the experts. Extensive experiments involving several well-known datasets for age estimation have demonstrated the superior performance of our proposed model over several existing state-of-the-art methods.

Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm

Skin lesion classification plays a crucial role in the early detection and diagnosis of various skin conditions. Recent advances in computer-aided diagnostic techniques have been instrumental in timely intervention, thereby improving patient outcomes, particularly in rural communities lacking specialized expertise. Despite the widespread adoption of convolutional neural networks (CNNs) in skin disease detection, their effectiveness has been hindered by the limited size and data imbalance of publicly accessible skin lesion datasets. In this context, a two-step hierarchical binary classification approach is proposed utilizing hybrid machine and deep learning (DL) techniques. Experiments conducted on the International Skin Imaging Collaboration (ISIC 2017) dataset demonstrate the effectiveness of the hierarchical approach in handling large class imbalances. Specifically, employing DenseNet121 (DNET) as a feature extractor and random forest (RF) as a classifier yielded the most promising results, achieving a balanced multiclass accuracy (BMA) of 91.07% compared to the pure deep-learning model (end-to-end DNET) with a BMA of 88.66%. The RF ensemble exhibited significantly greater efficiency than other machine-learning classifiers in aiding DL to address the challenge of learning with limited data. Furthermore, the implemented predictive hybrid hierarchical model demonstrated enhanced performance while significantly reducing computational time, indicating its potential efficiency in real-world applications for the classification of skin lesions.

Understanding cheese ripeness: An artificial intelligence-based approach for hierarchical classification

Within the contemporary dairy industry, the effective monitoring of cheese ripeness constitutes a critical yet challenging task. This paper proposes the first public dataset encompassing images of cheese wheels that depict various products at distinct stages of ripening and introduces an innovative hybrid approach, integrating machine learning and computer vision techniques to automate the detection of cheese ripeness. By leveraging deep learning and shallow learning techniques, the proposed method endeavors to overcome the limitations associated with conventional assessment methodologies. It aims to provide automation, precision, and consistency in the evaluation of cheese ripeness, delving into a hierarchical classification for the simultaneous classification of distinct cheese types and ripeness levels and presenting a comprehensive solution to enhance the efficiency of the cheese production process. By employing a lightweight hierarchical feature aggregation methodology, this investigation navigates the intricate landscape of preprocessing steps, feature selection, and diverse classifiers. We report a noteworthy achievement, attaining a best F-measure score of 0.991 through the merging of features extracted from EfficientNet and DarkNet-53, opening the field to concretely address the complexity inherent in cheese quality assessment.

An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems

Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales.

A new sentence embedding framework for the education and professional training domain with application to hierarchical multi-label text classification

In recent years, Natural Language Processing (NLP) has made significant advances through advanced general language embeddings, allowing breakthroughs in NLP tasks such as semantic similarity and text classification. However, complexity increases with hierarchical multi-label classification (HMC), where a single entity can belong to several hierarchically organized classes. In such complex situations, applied on specific-domain texts, such as the Education and professional training domain, general language embedding models often inadequately represent the unique terminologies and contextual nuances of a specialized domain. To tackle this problem, we present HMCCCProbT, a novel hierarchical multi-label text classification approach. This innovative framework chains multiple classifiers, where each individual classifier is built using a novel sentence-embedding method BERTEPro based on existing Transformer models, whose pre-training has been extended on education and professional training texts, before being fine-tuned on several NLP tasks. Each individual classifier is responsible for the predictions of a given hierarchical level and propagates local probability predictions augmented with the input feature vectors to the classifier in charge of the subsequent level. HMCCCProbT tackles issues of model scalability and semantic interpretation, offering a powerful solution to the challenges of domain-specific hierarchical multi-label classification. Experiments over three domain-specific textual HMC datasets indicate the effectiveness of HMCCCProbT to compare favorably to state-of-the-art HMC algorithms in terms of classification accuracy and also the ability of BERTEPro to obtain better probability predictions, well suited to HMCCCProbT, than three other vector representation techniques.

A New Method for Bare Permafrost Extraction on the Tibetan Plateau by Integrating Machine Learning and Multi-Source Information

Bare permafrost refers to permafrost with almost no vegetation on the surface, which is an essential part of the ecosystem of the Tibetan Plateau. An accurate extraction of the boundaries of bare permafrost is vital for studying how it is being impacted by climate change. The accuracy of permafrost and bare land distribution maps is inadequate, and the spatial and temporal resolution is low. This is due to the challenges associated with obtaining significant amounts of data in high-altitude and alpine regions and the limitations of current mapping techniques in effectively integrating multiple factors. This study introduces a novel approach to extracting information about the distribution of bare permafrost. The approach introduced here involves amalgamating a sample extraction method, the fusion of multi-source remote sensing information, and a hierarchical classification strategy. Initially, the available multi-source permafrost data, expert knowledge, and refinement rules for training samples are integrated to produce extensive and consistent permafrost training samples. Using the random forest method, these samples are then utilized to create features and classify permafrost. Subsequently, a methodology utilizing a hierarchical classification approach in conjunction with machine learning techniques is implemented to identify an appropriate threshold for fractional vegetation cover, thereby facilitating the extraction of bare land. The bare permafrost boundary is ultimately derived through layer overlay analysis. The permafrost classification exhibits an overall accuracy of 90.79% and a Kappa coefficient of 0.806. The overall accuracies of the two stratified extractions in bare land were 97.47% and 96.99%, with Kappa coefficients of 0.954 and 0.911. The proposed approach exhibits superiority over the extant bare land and permafrost distribution maps. It is well-suited for retrieving vast bare permafrost regions and is valuable for acquiring bare permafrost distribution data across a vast expanse. It offers technical assistance in acquiring extended-term data on the distribution of exposed permafrost on the Tibetan Plateau. Furthermore, it facilitates the elucidation of the impact of climate change on exposed permafrost.

A hierarchical fine-grained classification approach for COVID-19 severity assessment based on CT images

The Coronavirus Disease 2019 (COVID-19) is still an ongoing health issue. Appropriate treatment is important for the recovery of COVID-19 patients. At present, numerous deep learning (DL) methods have been applied to the classification of COVID-19 based on computerized tomography (CT) images. However, most of these methods directly extract visual features from the entire CT image, leading to the inclusion of irrelevant background information and making it hard to extract critical features about lesions. Thus, we propose a hierarchical fine-grained classification approach for automatic severity assessment of COVID-19 patients. The CT images are analyzed from coarse to fine in two stages, to focus on the critical features gradually. In the first stage, we detect the lesion regions from the CT image utilizing deep learning based model. In the second stage, we extract critical features from lesion regions and make a severity grading by fine-grained classification model. The experimental results show that the proposed method achieves the classification results of 95.04%, 93.98%, 93.46% and 93.72% in the metrics of accuracy, precision, recall and F1-Score, respectively.

GIS-Based Multi-Criteria Analysis for Selecting Suitable Areas for Urban Green Spaces in Abomey-Calavi District, Southern Benin

Green spaces contribute to a significant improvement in quality of life and maintain the sustainability of cities. In Benin, despite the political willingness regarding greening, municipalities are experiencing technical issues in finding suitable spaces to achieve this goal. This study should therefore be applicable to many other towns in Benin. It aims at identifying suitable areas for green spaces to integrate landscaping into urban planning in Abomey-Calavi district. Multi-criteria analysis combining GIS and the hierarchical classification approach was performed. Six factors (land use, altitude, slope, distance from main roads, proximity to urban centres and distance from flood zones, water bodies) were combined using the ArcGIS “Spatial Analysis” extension to generate a map of green space suitability. The results show large areas of land, of which 23.27% is very suitable and 26.06% is suitable for landscaping in this municipality. The ranking of the factors revealed that elevation, proximity to road networks, large conurbations and distance from wetlands accounted for 18%, compared with 14% for the other factors regarding the study environment. However, the use of these results must take into account the existing inhabited areas for a good site selection. These outputs provide guidance for decision-makers in choosing suitable sites for green spaces and integrating them into sustainable development.

A certainty-based approach for dynamic hierarchical classification of product order satisfaction

E-commerce companies collaborate with retailers to sell products via their platforms, making it increasingly important to preserve platform quality. In this paper, we contribute by introducing a novel method to predict the quality of product orders shortly after they are placed. By doing so, platforms can act fast to resolve bad quality orders and potentially prevent them from happening. This introduces a trade-off between accuracy and timeliness, as the sooner we predict, the less we know about the status of a product order and, hence, the lower the reliability. To deal with this, we introduce the Hierarchical Classification Over Time (HCOT) algorithm, which dynamically classifies product orders using top-down, non-mandatory leaf-node prediction. We enforce a blocking approach by proposing the Certainty-based Automated Thresholds (CAT) algorithm, which automatically computes optimal thresholds at each node. The resulting CAT-HCOT algorithm has the ability to provide both accurate and timely predictions by classifying a product order's quality on a daily basis if the classification reaches a predefined certainty. CAT-HCOT obtains a predictive accuracy of 94%. Furthermore, CAT-HCOT classifies 40% of product orders on the order date itself, 80% within five days after the order date, and 100% of product orders after 10 days.

A hierarchical automatic phoneme recognition model forHindi‐Devanagariconsonants using machine learning technique

AbstractA phoneme is perceptually the smallest distinct sound unit distinguished among words in a particular language. Every language has its own set of phonemes, and all the words are ordered sequences of phonemes. Therefore, phoneme recognition is essential to automatic speech recognition (ASR) systems. Phonemes of a language can be classified together using a single machine learning (ML) model through the direct classification (also known as thebaselineorflatclassification) approach. However, it is observed that the performance of such phoneme recognition degrades with the increase in the number of phoneme classes. The challenge is pronounced in languages with a larger number of phoneme classes, like Hindi, which has 48 phonemes. In this paper, we propose a speaker‐independent hierarchical classification approach for 33 Hindi‐Devanagari consonants/phonemes using cepstral features with ML techniques like support vector machine (SVM), random forest (RF) and fully connected deep neural network (DNN). In this hierarchical approach, a given phoneme is classified into successive subgroups until the particular phoneme class is identified. To perform the classification task, a binary or multi‐class classifier is invoked for each internal (non‐leaf) node in the hierarchy tree. Our model identified pairs ofOptimal Feature Sets(based onmutual information) and thebest suitable ML classifierfor each internal decision node in the hierarchy using 10‐fold cross‐validation to help in efficient classification. Our proposed hierarchical model leads to better accuracy and 57% improved performance for phoneme recognition compared with the non‐hierarchical, that is, thedirect classificationapproach.

Hierarchical strategy for sEMG classification of the hand/wrist gestures and forces of transradial amputees.

The myoelectric control strategy, based on surface electromyographic signals, has long been used for controlling a prosthetic system with multiple degrees of freedom. Several methods classify gestures and force levels but the simultaneous real-time control of hand/wrist gestures and force levels did not yet reach a satisfactory level of effectiveness. In this work, the hierarchical classification approach, already validated on 31 healthy subjects, was adapted for the real-time control of a multi-DoFs prosthetic system on 15 trans-radial amputees. The effectiveness of the hierarchical classification approach was assessed by evaluating both offline and real-time performance using three algorithms: Logistic Regression (LR), Non-linear Logistic Regression (NLR), and Linear Discriminant Analysis (LDA). The results of this study showed the offline performance of amputees was promising and comparable to healthy subjects, with mean F1 scores of over 90% for the "Hand/wrist gestures classifier" and 95% for the force classifiers, implemented with the three algorithms with features extraction (FE). Another significant finding of this study was the feasibility of using the hierarchical classification strategy for real-time applications, due to its ability to provide a response time of 100 ms while maintaining an average online accuracy of above 90%. A possible solution for real-time control of both hand/wrist gestures and force levels is the combined use of the LR algorithm with FE for the "Hand/wrist gestures classifier", and the NLR with FE for the Spherical and Tip force classifiers.

A machine learning approach for hierarchical classification of software requirements

Context:Classification of software requirements into different categories is a critically important task in requirements engineering (RE). Developing machine learning (ML) approaches for requirements classification has attracted great interest in the RE community since the 2000s. Objective:This paper aims to address two related problems that have been challenging real-world applications of ML approaches: the problems of class imbalance and high dimensionality with low sample size data (HDLSS). These problems can greatly degrade the classification performance of ML methods. Methods:The paper proposes HC4RC, a novel ML approach for multiclass classification of requirements. HC4RC solves the aforementioned problems through semantic-role based feature selection, dataset decomposition and hierarchical classification. We experimentally compare the effectiveness of HC4RC with three closely related approaches — two of which are based on a traditional statistical classification model whereas one using an advanced deep learning model. Results:Our experiment shows: (1) The class imbalance and HDLSS problems present a challenge to both traditional and advanced ML approaches. (2) The HC4RC approach is simple to use and can effectively address the class imbalance and HDLSS problems compared to similar approaches. Conclusion:This paper makes an important practical contribution to addressing the class imbalance and HDLSS problems in multiclass classification of software requirements.

Dealing with Unbalanced Data in Leaf Disease Detection: A Comparative Study of Hierarchical Classification, Clustering-based Undersampling and Reweighting-based Approaches

Precision agriculture plays a crucial role in optimizing crop yield, reducing environmental impact, and ensuring sustainable agricultural practices. Early detection and accurate diagnosis of leaf diseases are essential for preventing significant losses in crop production and maintaining food security. However, the inherent challenge of class imbalance in leaf disease datasets poses a significant obstacle for machine learning algorithms. In this paper, we explore and compare different techniques for handling class imbalance in leaf disease detection to improve the accuracy and reliability of machine learning models in the context of precision agriculture. We investigated the performance of different methods for leaf disease detection using the challenging New Plant Diseases Dataset (NPDD), which consists of image-based plant leaves. Our experiments reveal promising results, particularly with the hierarchical approach, achieving an accuracy of 97.17%. The outcomes of our study contribute to the growing body of knowledge in precision agriculture by providing a comprehensive analysis of techniques for handling class imbalance in leaf disease detection. Furthermore, our findings serve as a valuable resource for researchers and practitioners in the field, offering guidance on selecting and implementing the most effective approaches to tackle class imbalance challenges and improving the overall performance and reliability of machine learning models in the domain of precision agriculture.

An Attention-Based Architecture for Hierarchical Classification With CNNs

Branch Convolutional Neural Nets have become a popular approach for hierarchical classification in computer vision and other areas. Unfortunately, these models often led to hierarchical inconsistency: predictions for the different hierarchy levels do not necessarily respect the class-subclass constraints imposed by the hierarchy. Several architectures to connect the branches have arisen to overcome this limitation. In this paper, we propose a more straightforward and flexible method: let the neural net decide how these branches must be connected. We achieve this by formulating an attention mechanism that dynamically determines how branches influence each other during training and inference. Experiments on image classification benchmarks show that the proposed method can outperform state-of-the-art models in terms of hierarchical performance metrics and consistency. Furthermore, although sometimes we found a slightly lower performance at the deeper level of the hierarchy, the model predicts much more accurately the ground-truth path between a concept and its ancestors in the hierarchy. This result suggests that the model does learn not only local class memberships but also hierarchical dependencies between concepts.

Classifying Sub-Parcel Grassland Management Practices by Optical and Microwave Remote Sensing

Grassland management practices and intensities are key factors influencing the quality and balance of their provisioning and regulating ecosystem services. Most European temperate grasslands are exploited through mowing, grazing, or a combination of both in relatively small management units. Grazing and mowing can however not be considered equivalent because the first is gradual and selective and the second is not. In this study, the aim is to differentiate grasslands in terms of management practices and to retrieve homogeneous management units. Grasslands are classified hierarchically, first through a pixel-based supervised classification to differentiate grazed pastures from mown hay meadows and then through an object-based mowing detection method to retrieve the timing and frequency of mowing events. A large field dataset was used to calibrate and validate the method. For the classification, 18 different input feature combinations derived from Sentinel-1 and Sentinel-2 were tested for a random forest classifier through a cross-validation scheme. The best results were obtained based on the Leaf Area Index (LAI) times series with cubic spline interpolation. The classification differentiated pastures (grazed) from hay meadows (mown) with an overall accuracy of 88%. The classification is then combined with the existing parcel delineation and high-resolution ancillary data to retrieve the homogeneous management units, which are used for the object-based mowing detection based on the Sentinel-1 coherence and Sentinel-2 NDVI. The mowing detection performances were increased thanks to the grassland mask, the management unit delineation, and the exclusion of pastures, reaching a precision of 93% and a detection rate of 82%. This hierarchical grassland classification approach allowed to differentiate three types of grasslands, namely pastures, and meadows (including mixed practices) with an early first mowing event and with a late first mowing event, with an overall accuracy of 79%. The grasslands could be further differentiated by mowing frequency, resulting in five final classes.

Unsupervised Hierarchical Classification Approach for Imprecise Data in the Breast Cancer Detection.

(1) Background: in recent years, a lot of the research of statistical methods focused on the classification problem in presence of imprecise data. A particular case of imprecise data is the interval-valued data. Following this research line, in this work a new hierarchical classification technique for multivariate interval-valued data is suggested for diagnosis of the breast cancer; (2) Methods: an unsupervised hierarchical classification method for imprecise multivariate data (called HC-ID) is performed for diagnosis of breast cancer (i.e., to discriminate between benign or malignant masses) and the results have been compared with the conventional (unsupervised) hierarchical classification approach (HC); (3) Results: the application on real data shows that the HC-ID procedure performs better HC procedure in terms of accuracy (HC-ID = 0.80, HC = 0.66) and sensitivity (HC-ID = 0.61, HC = 0.08). In the results obtained by the usual procedure, there is a high degree of false-negative (i.e., benign cancer diagnosis in malignant status) affected by the high degree of variability (i.e., uncertainty) characterizing the worst data.

Geotagging Social Media Posts to Landmarks Using Hierarchical BERT (Student Abstract)

Geographical information provided in social media data is useful for many valuable applications. However, only a small proportion of social media posts are explicitly geotagged with their posting locations, which makes the pursuit of these applications challenging. Motivated by this, we propose a 2-level hierarchical classification method that builds upon a BERT model, coupled with textual information and temporal context, which we denote HierBERT. As far as we are aware, this work is the first to utilize a 2-level hierarchical classification approach alongside BERT and temporal information for geolocation prediction. Experimental results based on two social media datasets show that HierBERT outperforms various state-of-art baselines in terms of accuracy and distance error metrics.

Camera model identification based on forensic traces extracted from homogeneous patches

A crucial challenge in digital image forensics is to identify the source camera model used to generate given images. This is of prime importance, especially for Law Enforcement Agencies in their investigations of Child Sexual Abuse Material found in darknets or seized storage devices.In this work, we address this challenge by proposing a solution that is characterized by two main contributions. It relies on the extraction of rather small homogeneous regions that we extract very efficiently from the integral image, and on a hierarchical classification approach with convolutional neural networks as the underlying models. We rely on homogeneous regions as they contain camera traces that are less distorted than regions with high-level scene content. The hierarchical approach that we propose is important for scaling up and making minimal modifications when new cameras are added. Furthermore, this scheme performs better than the traditional single classifier approach. By means of thorough experimentation on the publicly available Dresden data set, we achieve an accuracy of 99.01% with 5-fold cross-validation on the ‘natural’ subset of this data set. To the best of our knowledge, this is the best result ever reported for Dresden data set.

Digital Hebrew Paleography: Script Types and Modes.

Paleography is the study of ancient and medieval handwriting. It is essential for understanding, authenticating, and dating historical texts. Across many archives and libraries, many handwritten manuscripts are yet to be classified. Human experts can process a limited number of manuscripts; therefore, there is a need for an automatic tool for script type classification. In this study, we utilize a deep-learning methodology to classify medieval Hebrew manuscripts into 14 classes based on their script style and mode. Hebrew paleography recognizes six regional styles and three graphical modes of scripts. We experiment with several input image representations and network architectures to determine the appropriate ones and explore several approaches for script classification. We obtained the highest accuracy using hierarchical classification approach. At the first level, the regional style of the script is classified. Then, the patch is passed to the corresponding model at the second level to determine the graphical mode. In addition, we explore the use of soft labels to define a value we call squareness value that indicates the squareness/cursiveness of the script. We show how the graphical mode labels can be redefined using the squareness value. This redefinition increases the classification accuracy significantly. Finally, we show that the automatic classification is on-par with a human expert paleographer.

COVID Detection From Chest X-Ray Images Using Multi-Scale Attention.

Deep learning based methods have shown great promise in achieving accurate automatic detection of Coronavirus Disease (covid) - 19 from Chest X-Ray (cxr) images.However, incorporating explainability in these solutions remains relatively less explored. We present a hierarchical classification approach for separating normal, non-covid pneumonia (ncp) and covid cases using cxr images. We demonstrate that the proposed method achieves clinically consistent explainations. We achieve this using a novel multi-scale attention architecture called Multi-scale Attention Residual Learning (marl) and a new loss function based on conicity for training the proposed architecture. The proposed classification strategy has two stages. The first stage uses a model derived from DenseNet to separate pneumonia cases from normal cases while the second stage uses the marl architecture to discriminate between covid and ncp cases. With a five-fold cross validation the proposed method achieves 93%, 96.28%, and 84.51% accuracy respectively over three large, public datasets for normal vs. ncp vs. covid classification. This is competitive to the state-of-the-art methods. We also provide explanations in the form of GradCAM attributions, which are well aligned with expert annotations. The attributions are also seen to clearly indicate that marl deems the peripheral regions of the lungs to be more important in the case of covid cases while central regions are seen as more important in ncp cases. This observation matches the criteria described by radiologists in clinical literature, thereby attesting to the utility of the derived explanations.