Hierarchical Classification Method Research Articles

A data decomposition-based hierarchical classification method for multi-label classification of contractual obligations for the purpose of their governance

Contract governance ensures that the agreed outcomes between customers and vendors are fulfilled. Information Technology (IT) outsourcing organizations enter thousands of contractual relationships each month leading to a high volume of business-critical contractual text that must be reviewed and deciphered for effective governance. The key to effective governance of contracts is a model that facilitates assigning ownership of the obligations to the right departments in an organization and allocating their accountability to the right stakeholders. For this, the contractual obligations need to be identified and classified so that details such as actions to be taken by departments in an organization and their ownership as per a given clause are brought out for the purpose of governance. In this paper, we present our work on automated extraction and classification of obligations present in Software Engineering (SE) contracts for the purpose of contracts governance. We propose a novel data decomposition-based hierarchical classification method for a multi-label classification of contractual obligations. We conducted experiments for a fine-grained automated classification of more than 55,000 statements from 50 large real-life SE contract documents received from a large vendor organization into 152 governance-specific classes. The results indicate that the proposed method can bring about a 7–8% improvement in accuracies when compared to state-of-the-art classification baselines such as BERT, RoBERTa, and generative models such as GPT-2.

Characterization of land use, from a social, economic and environmental dynamics

Characterization of land use, from a social, economic and environmental dynamics

Hierarchical Text Classification and Its Foundations: A Review of Current Research

While collections of documents are often annotated with hierarchically structured concepts, the benefits of these structures are rarely taken into account by classification techniques. Within this context, hierarchical text classification methods are devised to take advantage of the labels’ organization to boost classification performance. In this work, we aim to deliver an updated overview of the current research in this domain. We begin by defining the task and framing it within the broader text classification area, examining important shared concepts such as text representation. Then, we dive into details regarding the specific task, providing a high-level description of its traditional approaches. We then summarize recently proposed methods, highlighting their main contributions. We also provide statistics for the most commonly used datasets and describe the benefits of using evaluation metrics tailored to hierarchical settings. Finally, a selection of recent proposals is benchmarked against non-hierarchical baselines on five public domain-specific datasets. These datasets, along with our code, are made available for future research.

Improving river medium–high flow estimation by CM Hierarchical Classification (CMHC) method using Sentinel-2 imagery

River discharge monitoring is one of the most important parts of water resources management. However, due to hydrological stations' high operating and maintenance costs, discharge monitoring data has gradually become scarce. Remote sensing technology has great potential to supplement observations in this field. The close relationship between the reflectance ratio of dry (C) and wet (M) pixels around the river in the near-infrared (NIR) band and river discharge has been effectively proved, and many CM-based methods have also achieved good results, but often underestimate medium–high flow in rivers. However, medium–high flow usually occurs during the rainy season or flood-prone periods, and their underestimation not only affects the risk assessment of possible or already occurred floods but may also cause damage to people's lives, property, and social assets. To improve this phenomenon, we proposed a new CM method called CM Hierarchical Classification (CMHC) based on the Sentinel-2 dataset and Google Earth Engine (GEE) cloud platform to monitor the river discharge at different classes and achieve high-precision discharge estimation at medium–high flow. The study was conducted at four stations along the Po River in Italy, where measured discharge data were used to derive the calibration curve and evaluate the performance of the estimated results. The Nash-Sutcliffe efficiency (NS) values for the estimates reached an average of 0.84 and up to 0.89, indicating the excellent performance of the method. Compared with existing methods, our method substantially improves the monitoring accuracy of medium–high flow of the river. Furthermore, additional experiments in the Han River in China also confirmed the applicability of the CMHC method, implying that this method can be used to improve the ability of Sentinel-2 to estimate river discharge, especially for medium–high flow, and provide support for river discharge monitoring on a global scale.

Graph Neural Network Based Multi-Label Hierarchical Classification for Disease Predictions in General Practice.

General practitioners are supposed to be better diagnostics to detect patients with serious diseases earlier, and conduct early interventions and appropriate referrals of patients. However, in the current general practice, primary general practitioners lack sufficient clinical experiences, and the correct rate of general disease diagnosis is low. To assist general practitioners in diagnosis, this paper proposes a multi-label hierarchical classification method based on graph neural network, which integrates medical knowledge and electronic health record (EHR) data to build a disease prediction model. The experimental results based on data consist of 231,783 visits from EHR show that the proposed model outperforms all baseline models in the general disease prediction task with a top-3 recall of 0.865. The interpretable results of the model can effectively help clinicians understand the basis of the model's decision-making.

Dynamics of coastal land-based aquaculture pond in China and Southeast Asia from 1990 to 2020

The extensive expansion of coastal land-based aquaculture ponds (CLAP) has resulted in substantial wetland loss. To effectively monitor the dynamics of CLAP on a large scale, critical spatial information derived from continuous datasets is essential. Here, we generate the inaugural map depicting the changes in aquaculture pond coverage across the coastal regions both of China and Southeast Asia from 1990 to 2020 by utilizing the long time-series Landsat archive with an object-oriented hierarchical classification method. We extracted coastal water bodies using the Otsu algorithm and established a water inundation frequency index to eliminate interference from temporary water facilities. This process allowed us to identify potential distribution areas of aquaculture ponds. To precisely map the spatial distribution of CLAP within these identified areas, we employed an object-oriented K-nearest neighbor approach with spectral, geometric, and water index features as classification features. The cumulative coverage of CLAP in China and Southeast Asia reached 23,121 km2 by 2020, marking a net increase of 12,700 km2 since 1990. Notably, China, Indonesia, and Vietnam emerged as the primary contributors to these changes within our study area. Regions exhibiting dense aquaculture activities are predominantly concentrated in the northern region above 10 degrees latitude and along the eastern coastlines of various countries. This aquaculture pond map reserves fine spatial detail by effectively distinguishing drainage channels, rice seedling fields, and embankments surrounding aquaculture ponds. It serves as a valuable resource for guiding coastal resource management and environmental protection initiatives.

Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

Timely and accurate rice spatial distribution maps play a vital role in food security and social stability. Early-season rice mapping is of great significance for yield estimation, crop insurance, and national food policymaking. Taking Tongjiang City in Heilongjiang Province with strong spatial heterogeneity as study area, a hierarchical K-Means binary automatic rice classification method based on phenological feature optimization (PFO-HKMAR) is proposed, using Google Earth Engine platform and Sentinel-1/2, and Landsat 7/8 data. First, a SAR backscattering intensity time series is reconstructed and used to construct and optimize polarization characteristics. A new SAR index named VH-sum is built, which is defined as the summation of VH backscattering intensity for specific time periods based on the temporal changes in VH polarization characteristics of different land cover types. Then comes feature selection, optimization, and reconstruction of optical data. Finally, the PFO-HKMAR classification method is established based on Simple Non-Iterative Clustering. PFO-HKMAR can achieve early-season rice mapping one month before harvest, with overall accuracy, Kappa, and F1 score reaching 0.9114, 0.8240 and 0.9120, respectively (F1 score is greater than 0.9). Compared with the two crop distribution datasets in Northeast China and ARM-SARFS, overall accuracy, Kappa, and F1 scores of PFO-HKMAR are improved by 0.0507–0.1957, 0.1029–0.3945, and 0.0611–0.1791, respectively. The results show that PFO-HKMAR can be promoted in Northeast China to enable early-season rice mapping, and provide valuable and timely information to different stakeholders and decision makers.

A Hierarchical Classification Method for High-accuracy Instruction Disassembly with Near-field EM Measurements

Electromagnetic (EM) fields have been extensively studied as potent side-channel tools for testing the security of hardware implementations. In this work, a low-cost side-channel disassembler that uses fine-grained EM signals to predict a program's execution trace with high accuracy is proposed. Unlike conventional side-channel disassemblers, the proposed disassembler does not require extensive randomized instantiations of instructions to profile them, instead relying on leakage-model-informed sub-sampling of potential architectural states resulting from instruction execution, which is further augmented by using a structured hierarchical approach. The proposed disassembler consists of two phases: (i) In the feature-selection phase, signals are collected with a relatively small EM probe, performing high-resolution scans near the chip surface, as profiling codes are executed. The measured signals from the numerous probe configurations are compiled into a hierarchical database by storing the min-max envelopes of the probed EM fields and differential signals derived from them, a novel dimension that increases the potency of the analysis. The envelope-to-envelope distances are evaluated throughout the hierarchy to identify optimal measurement configurations that maximize the distance between each pair of instruction classes. (ii) In the classification phase, signals measured for unknown instructions using optimal measurement configurations identified in the first phase are compared to the envelopes stored in the database to perform binary classification with majority voting, identifying candidate instruction classes at each hierarchical stage. Both phases of the disassembler rely on a four-stage hierarchical grouping of instructions by their length, size, operands, and functions. The proposed disassembler is shown to recover ∼97–99% of instructions from several test and application benchmark programs executed on the AT89S51 microcontroller.

Розвиток понятійного апарату у сфері обліку і оподаткування цифрових активів

Introduction. In recent years, the digital economy has become the main driver of economic growth both at the global and national levels. Within this paradigm, a number of socio-economic challenges are being overcome: creating new jobs, ensuring digital inclusion of people around the world. Problem Statement. The national institutional environment in the field of the digital economy is currently under development, in particular in terms of consistency of conceptual apparatus. The purpose of the article is to analyze the conceptual and categorical apparatus used in the regulatory and legal framework for accounting and taxation of objects arising from the activities of business entities in the digital economy, and to determine the directions of its development. Methods: institutional approach; comparative analysis; synthesis; hierarchical classification method. Results. A bibliographic analysis of scientific and professional publications showed that all categories of “non-fungible token”, “virtual asset”, “cryptoasset” are inherently digital assets, i.e. assets that are stored or transmitted in digital format and have economic or cultural value. The global nature of digital assets has led to the need for international coordination on their legal status and resolution mechanisms. Such digital assets as virtual assets, cryptoassets and their subtypes are regulated at different levels: international (FATF, OECD); regional (EU); national (UK, Ukraine). The presented review of the documents defining the concepts of “digital asset”, “cryptoasset” and “virtual asset” at the international and regional levels, as well as in individual countries, demonstrates that there is currently no generally accepted definition of these terms. Conclusions. The conducted study of the features in forming the conceptual and terminological apparatus of accounting and taxation of objects, the appearance of which is determined by the activities of economic entities under the digital economy, in regulatory documents at various levels showed that when developing institutional support for the regulation of the circulation of digital assets and operations with them in Ukraine the approach used by the FATF was chosen. In terms of its content, the category of “virtual assets” corresponds to the interpretation of the category of “cryptoassets” laid down by European legislation. The author identifies terminological inconsistencies in the Ukrainian legal framework that require further resolution through a cross-analysis of legislation to unify approaches to the interpretation of digital economy objects.

Hierarchical classification for acoustic scenes using deep learning

Acoustic Scene Classification (ASC) aims to obtain the sound environment by analyzing audio signals. Due to the low complexity and acquisition cost of audio signals, ASC has enormous potential in various applications, such as audio-based surveillance, smart cities/homes, and robotics. Recently, various methods have been proposed for ASC and achieved good performance. However, when they are used to address complex ASC problems, most of them suffer from the low-performance problem. In this paper, we propose to use hierarchical classification methods to replace the conventional flat approach in ASC applications, which utilizes the class hierarchy to optimize classification performance. In particular, we investigate the ASC problem under the framework of hierarchical classification. Firstly, to improve classification performance, three hierarchical classification methods introducing the class hierarchy of acoustic scenes are proposed for ASC. Moreover, to fully utilize the class hierarchy, a hybrid hierarchical classification method, and an optimal late fusion-based hierarchical method are proposed, which are based on the flexibility and simplification of hierarchical classification. The experiments demonstrate the efficacy of hierarchical ASC systems for performance improvement, and the best system achieves an accuracy of 78.86% on the DCASE 2020 Task1A dataset, resulting in accuracy gains of 24.76% and 8.52% absolute over the DCASE 2020 Task 1A baseline and the conventional non-hierarchical method, respectively.

Hierarchical long-tailed classification based on multi-granularity knowledge transfer driven by multi-scale feature fusion

Long-tailed learning is attracting increasing attention due to the unbalanced distributions of real-world data. The aim is to train well-performing depth models. Traditional knowledge transfer methods for long-tailed learning are classified into feature-based horizontal knowledge transfer (HKT) and class-based vertical knowledge transfer (VKT). HKT transfers head-to-tail feature knowledge from different classes to improve classification performance when there are few tail classes. However, HKT easily leads to invalid transfer due to the deviation caused by the difference between the knowledge of head and tail classes. Fortunately, the class space has a multi-grained relationship and can form a multi-granularity knowledge graph (MGKG), which can be recast as coarse-grained and fine-grained losses to guide VKT. In this paper, we propose a hierarchical long-tailed classification method based on multi-granularity knowledge transfer (MGKT), which vertically transfers knowledge from coarse- to fine-grained classes. First, we exploit the semantic information of classes to construct an MGKG, which forms an affiliation of fine- and coarse-grained classes. Fine-grained knowledge can inherit coarse-grained knowledge to reduce transfer bias with the help of MGKG. We then propose a multi-scale feature fusion network, which aims to fully mine the rich information of the features to drive MGKT. Experiments show that the proposed model outperforms several state-of-the-art models in classifying long-tailed data. For example, our model performed 4.46% better than the next-best model on the SUN-LT dataset.

Automatic detection of single street trees from airborne LiDAR data based on point segmentation methods

As a primary element of urban ecosystem, street trees are very essential for environmental quality and aesthetic beauty of urban landscape. Street trees play a crucial role in everyday life of city inhabitants and therefore, comprehensive and accurate inventory information for street trees is required. In this research, an automatic method is proposed to detect single street trees from airborne Light Detection and Ranging (LiDAR) point cloud instead of traditional field work or photo interpretation. Firstly, raw LiDAR point cloud data have been classified to obtain high vegetation class with a hierarchical rule-based classification method. Then, the LiDAR points in high vegetation class were segmented with mean shift and Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to acquire single urban street trees in the Davutpasa Campus of Yildiz Technical University, Istanbul, Turkey. The accuracy assessment of the acquired street trees was also conducted using completeness and correctness analyses. The acquired results from urban study area approved the success of the proposed point-based approach for automatic detection of single street trees using LiDAR point cloud.

Hierarchical classification method of electricity consumption behaviour of power users based on combination model

Hierarchical classification method of electricity consumption behaviour of power users based on combination model

Profile of publications on tourism with elderly people in Brazil

Tourism and leisure are sectors that have been awakening the demand of elderly consumers. The aim of this article was to map the literature on Brazilian productions on tourism and human aging. For this purpose, a Scoping review was carried out using the PRISMA method. Data collection did not have publication time delimitation and CAPES and BDTD databases were used. The 46 selected documents were analyzed by IRaMuTeQ. The results were presented using the descending hierarchical classification method and the word cloud. The corpus was divided into 365 text segments and 59.06% of these data were evaluated. 12,723 different elements were analyzed. Descending hierarchical classification separated the corpus into five classes of textual elements. The importance of these publications for a national panorama and future analyzes of the development of this theme in our society are highlighted.

AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle

With the enhanced interoperability of information among vehicles, the demand for collaborative sharing among vehicles increases. Based on blockchain, the classical consensus algorithms in collaborative IoV (Internet of Vehicle), such as PoW (Proof of Work), PoS (Proof of Stake), and DPoS (Delegated Proof of Stake), only consider the node features, which is hard to adapt to the immediacy and flexibility of vehicles. On the other hand, classical consensus algorithms often require mass computing, which undoubtedly increases the communication overhead, resulting in the inability to achieve collaborative IoV under asymmetric networks. Therefore, proposing a low failure rate consensus algorithm that takes into account running time and energy consumption becomes a major challenge in IoV applications. This paper proposes an AI-enabled consensus algorithm with vehicle features, combining vehicle-based metrics and neural networks. First, we introduce vehicle-based metrics such as vehicle online time, performance, and behavior. Then, we propose an integral model and a hierarchical classification method, which combine with a BP neural network to obtain the optimal solution for interconnection. Among them, we also use Informer to predict the future online duration of vehicles, which effectively solves the situation that the primary node vehicle drops off in collaborative IoV. Finally, the experimentations show that the vehicle-based metrics eliminate the problem of the primary node vehicle being offline, which realizes the collaborative IoV considering vehicle features. Meanwhile, it reduces the vehicle network system delay and energy consumption.

Semantic guided level-category hybrid prediction network for hierarchical image classification

Hierarchical classification (HC) assigns each object with multiple labels organized into a hierarchical structure. The existing deep learning-based HC methods usually predict an instance starting from the root node until a leaf node is reached. However, in the real world, images impaired by noise, occlusion, blur, or low resolution may not provide sufficient information for the classification at subordinate levels. To address this issue, we propose a novel Semantic Guided level-category Hybrid Prediction Network (SGHPN) that can jointly perform the level and category prediction in an end-to-end manner. SGHPN comprises two modules: a visual transformer that extracts feature vectors from the input images, and a semantic guided cross-attention module that uses categories word embeddings as queries to guide learning category-specific representations. In order to evaluate the proposed method, we construct two new datasets in which images are at a broad range of quality and thus are labeled to different levels (depths) in the hierarchy according to their individual quality. Experimental results demonstrate the effectiveness of our proposed HC method.

Spatiotemporal changes of wetlands in China during 2000–2015 using Landsat imagery

China has abundant wetland resources; however, wetland ecosystems are experiencing critical challenges owing to continuous anthropogenic activities and climate change. Therefore, rapid and accurate mapping of wetlands and analysis of the drivers of spatiotemporal changes in wetlands are of great importance for wetland resource conservation. In this study, we proposed a comprehensive method for wetland mapping in China based on Landsat images, in which inland wetlands were identified by the object-oriented and hierarchical classification method, and coastal wetlands were extracted by visual interpretation based on sea boundaries and sea level nodes. Additionally, partial least squares structural equation modelling was utilized to exploit the mechanisms of spatiotemporal variability in wetlands during 2000–2015. The application of the comprehensive method to Landsat TM/OLI resulted in wetland maps of China with overall classification accuracies above 85%. The national wetland maps demonstrated that the wetland areas of China in 2000 and 2015 were 476,305.68 and 456,262.01 km2, respectively, and swamps accounted for the largest proportion of the total wetland area in China (over 38%). Statistics show that wetlands are mainly located in the northern inland areas, such as Tibet, Inner Mongolia, and Xinjiang Autonomous Region, as well as the Qinghai and Heilongjiang Provinces. Wetland changes were concentrated in the Tibetan Plateau, Loess Plateau, Song-Liao Plain, and middle and lower reaches of the Yangtze River. Human activities and climate change were the primary contributors to wetland changes in China. These two factors can not only directly influence wetland changes but also can be indirectly influenced by other factors, such as soil and topography. The results of this study are intended to provide basic data for monitoring and conserving wetland resources in China.

Mapping Diverse Paddy Rice Cropping Patterns in South China Using Harmonized Landsat and Sentinel-2 Data

Paddy rice cropping patterns (PRCPs) play important roles in both agroecosystem modeling and food security. Although paddy rice maps have been generated over several regions using satellite observations, few studies have focused on mapping diverse smallholder PRCPs, which include crop rotation and are dominant cropping structures in South China. Here, an approach called the feature selection and hierarchical classification (FSHC) method was proposed to effectively identify paddy rice and its rotation types. Considering the cloudy and rainy weather in South China, a harmonized Landsat and Sentinel-2 (HLS) surface reflectance product was employed to increase high-quality observations. The FSHC method consists of three processes: cropping intensity mapping, feature selection, and decision tree (DT) model development. The FSHC performance was carefully evaluated using crop field samples obtained in 2018 and 2019. Results suggested that the derived cropping intensity map based on the Savitzky–Golay (S-G) filtered normalized difference vegetation index (NDVI) time series was reliable, with an overall accuracy greater than 93%. Additionally, the optimal spectral (i.e., normalized difference water index (NDWI) and land surface water index (LSWI)) and temporal (start-of-season (SOS) date) features for distinguishing different PRCPs were successfully identified, and these features are highly related to the critical growth stage of paddy rice. The developed DT model with three hierarchical levels based on optimal features performed satisfactorily, and the identification accuracy of each PRCP can be achieved approximately 85%. Furthermore, the FSHC method exhibited similar performances when mapping PRCPs in adjacent years. These results demonstrate that the proposed FSHC approach with HLS data can accurately extract diverse PRCPs over fragmented croplands; thus, this approach represents a promising opportunity for generating refined crop type maps.

H-CNN combined with tissue Raman spectroscopy for cervical cancer detection

Cervical cancer is one of the most common cancers with a long latent period and slow onset process. Early and accurate identification of the stage of cervical cancer can significantly improve the cure rate and patient survival time. In this study, we collected 699 Raman spectral data of tissue sections from 233 different patients. We analyzed and compared the characteristics and differences of the mean Raman spectra of the seven tissues and pointed out the main differences in the biochemical composition of the seven tissues. In this study, 1D hierarchical convolutional neural network (H-CNN) is proposed by integrating the prior knowledge of hierarchical classification relations with the research of deep learning in Raman spectroscopy. H-CNN is based on CNN and is added with three network branches. Hierarchical classification is performed from coarse to fine for tissue samples of cervicitis, Low-grade Squamous Cell Carcinoma, High-grade Squamous Cell Carcinoma, Well Differentiated Squamous Cell Carcinoma, Moderately Differentiated Squamous Cell Carcinoma, Poorly Differentiated Squamous Cell Carcinoma and cervical adenocarcinoma. To evaluate the recognition performance of H-CNN, we compared it with traditional methods such as Bayesian classifier (NB), decision tree classifier (DT), support vector machine classifier (SVM) and CNN. The experimental results show that H-CNN can accurately identify different classes of tissue sections and has apparent advantages in several aspects such as recognition accuracy, stability and sensitivity compared with the other four traditional recognition methods. The classification Macro-Accuracy of H-CNN can reach 94.91%, Macro-Recall can reach 95.31%, Macro-F1 can reach 95.23%, and Macro-AUC can reach 97.35%. The hierarchical classification method proposed in this study can diagnose patients more accurately. This could lay the foundation for further research on Raman spectroscopy as a clinical diagnostic method for cervical cancer.

Deep Attention-based Classification of Plant Images with Hierarchical Similarity and Imbalanced Distribution

—Standard datasets in deep learning-basedimage classification usually provide two favorable conditions for design: preservation of visual homogeneity of each category and uniform distribution of sample images. These conditions are not assured in a dataset of plant images. Two different species of plants under the same genus look very similar and the number of collectible images has a large variation over species.Visual similarity, however, can be turned into advantage in hierarchical approach, by assigning two-fold labels of species and genus, in two phases ofrough classification of genera and fine classification of species. We propose a hierarchical classification in which the concatenation scheme is augmented with a channel attention which focuses on the sibling relation of species. We compared our method with flat classification and conventional hierarchical classification. The test was on a PlantNet-300K dataset 300k images, composed of 303 genera and 1081 species. In experimental results, the channel attention layers lead to stable discerningof the minute difference among visually similar species. The proposed hierarchical classification method outperforms both the flat classification and the conventional hierarchical classification.