Composite kernel based SVM for hierarchical multi-label gene function classification

Traditional classification tasks deal with assigning instances to a single label. However, some real world databases classes are structured in a hierarchy and its instances can have their classes associated with two or more paths in the hierarchical structure. In this case, such situations are referred as hierarchical multi-label classification problems. The purpose of this paper is to explore the concept of hierarchical multi-label classification problems and present a solution based on Learning Classifier Systems (LCS) to solve this kind of problem. The Hierarchical Learning Classifier System Multi-label (HLCS-Multi) proposed, presents a comprehensive solution to hierarchical multi-label classification problems building a global classifier to predict all classes in the application domain.KeywordsHierarchical Multi-label Classification ProblemsLearning Classifier SystemsProtein Function

In this study, a hierarchical multi-label classification method called HFFNet-2d is proposed for the automatic classification of scanning electron microscope (SEM) images of metal failure. The method combines the advantages of convolutional neural networks (CNN) and Vision Transformers (ViT) to effectively realize hierarchical feature extraction and classification of SEM images of fracture morphologies, enabling accurate identification of metal failure mechanisms at different scales. The dataset of high-quality SEM images in this work is sourced from reputable materials science publications for its comprehensive coverage of various failure modes and its suitability for training and validating the hierarchical multi-label classification model. The HFFNet-2d model can achieve a high accuracy of 97.71% in the first-level classification and 92.62% in the second-level sub-category identification. This performance surpasses the human experts on the same task. To ensure that the model predictions are sufficiently reliable, a multi-level gradcam algorithm is also introduced for checking the regions of interest of the Hierarchical model at two levels and the comparisons are made with human experts. It is anticipated that the optimization and extension of HFFNet-2d are conducive in diverse material systems and application scenarios to accelerate the intelligent process of material development and failure analysis, ultimately supporting the design of reliable and high-performance engineering materials.

The classification problems described in the Machine Learning literature usually relate to the classification of data in which each example is associated to a class belonging to a finite set of classes, all at the same level. However, there are classification issues, of a hierarchical nature, where the classes can be either subclasses or super classes of other classes. In many hierarchical problems, one or more examples may be associated with more than one class simultaneously. These problems are known as hierarchical multi-label classification (HMC) problems. In this work, the ML-KNN algorithm was used to predict hierarchical multi-label problems, in order to determine the number of classes that can be assigned to an example. Through the experiments performed on 10 protein function databases and the statistical analysis of the results, it can be shown that the adaptations performed in the ML-KNN algorithm brought significant performance improvements based on the hierarchical precision and recall metrics Hierarchical.

Deep hiearchical multi-label classification applied to chest X-ray abnormality taxonomies.

Hierarchical Classification is a very important classification task for arranging data in a hierarchical structure. Hierarchical arrangement of data is one of the best methods to achieve better understanding of complex data. In this paper, we propose the HMAC method to perform Hierarchical Multi-label Associative Classification. This method uses multiple and negative rules to predict class-set and to filter exceptional cases in order to improve both accuracy and explanatory ability of the resulting classifier. Redundant rule pruning method for negative rules and rule ranking method for hierarchical associative classification are developed. Moreover, we propose a rule evaluation measure, Sim, that tread-off between F-measure and Jaccard's coefficient to encode hierarchical structure meaning of actual and predicted node collections. We show that Sim is robust and simple rule evaluation measure. Experimental results show that HMAC improves accuracy and explanatory ability of hierarchical classifiers compared with various rule ranking and pruning techniques.

In this paper, we present a hierarchical multi-label classification system for visual concepts detection and image annotation. Hierarchical multi-label classification (HMLC) is a variant of classification where an instance may belong to multiple classes at the same time and these classes/labels are organized in a hierarchy. The system is composed of two parts: feature extraction and classification/annotation. The feature extraction part provides global and local descriptions of the images. These descriptions are then used to learn a classifier and to annotate an image with the corresponding concepts. To this end, we use predictive clustering trees (PCTs), which are able to classify target concepts that are organized in a hierarchy. Our approach to HMLC exploits the annotation hierarchy by building a single predictive clustering tree that can simultaneously predict all of the labels used to annotate an image. Moreover, we constructed ensembles (random forests) of PCTs, to improve the predictive performance. We tested our system on the image database from the ImageCLEF@ICPR 2010 photo annotation task. The extensive experiments conducted on the benchmark database show that our system has very high predictive performance and can be easily scaled to large number of visual concepts and large amounts of data.

Hierarchical Multi-label Classification using Fully Associative Ensemble Learning

Hierarchical multi-label text classification (HMTC) has become rather challenging when it requires handling large sets of closely related categories. We present a novel software that provides a hybrid embedding-based text representation for HMTC, shortened as HE-HMTC. It made full use of categories’ structure and their labels semantics to enrich the text representation, and therefore attempted to improve the classification performance of the text. In addition, HE-HMTC facilitates improving the accuracy of HMTC tasks. Moreover, our HE-HMTC can easily be generalized in other hierarchical classification tasks and achieve superior performance.

Assigning the subject heading is a significant work of the librarian. Subject heading represents the overall contents of the books which is a great way to easily find the books related directly to the user’s needs. But a book can be related to more than one subject and Some librarians are not experts in every discipline. They take more time on this process. In this paper, our research has a small dataset, so the multi-label classification technique is unsuitable for this work. Therefore, we propose the hierarchical multi-label classification to help them assign subject heading to the book from a title and a table of contents. We also compare the performance of three techniques: Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), to select the best classification technique. According to the results, Random Forest technique is the best one for the first hierarchy classifiers with the highest F1 of 0.908. The second hierarchy classifiers that uses Random Forest technique attains the F1 score of 0.871.

Hierarchical multilabel classification (HMC) assigns multiple labels to each instance with the labels organized under hierarchical relations. In ship classification in remote sensing images, depending on the expert knowledge and image quality, the same type of ships in different remote sensing images may be annotated with different class labels from coarse to fine levels such as merchant ship (MS) or container ship (CTS). In this article, we propose a novel deep network with two output channels and their associated loss functions to learn an HMC classifier using samples labeled at different levels in the hierarchy. In the proposed network, a hierarchy and exclusion (HEX) graph is introduced to model the label hierarchy, which satisfies hierarchical constraints by encoding semantic relations between any two labels. The output nodes of the first channel are organized according to the HEX graph, and its corresponding probabilistic classification loss is built to reflect the hierarchical structure of the HEX graph. On the other hand, the output nodes of the second channel only represent the finest grained (last level in the hierarchy) classes, and its multiclass cross-entropy loss is designed to enhance the discriminative power of the HMC classifier on the last level labels, which is also compatible with constraints in the HEX graph. The combination of these two losses from two output channels can effectively transfer the hierarchical information of ship taxonomy during network training. Experimental results on two commonly used ship datasets demonstrate that the proposed method outperforms the state-of-the-art HMC approaches, and is especially advantageous when trained with fewer fine-grained samples.

The existence of annotated text corpora is essential for the development of public health services and tools based on natural language processing (NLP) and text mining. Recently organized biomedical NLP shared tasks have provided annotated corpora related to different biomedical entities such as genes, phenotypes, drugs, diseases and chemical entities. These are needed to develop named-entity recognition (NER) models that are used for extracting entities from text and finding their relations. However, to the best of our knowledge, there are limited annotated corpora that provide information about food entities despite food and dietary management being an essential public health issue. Hence, we developed a new annotated corpus of food entities, named FoodBase. It was constructed using recipes extracted from Allrecipes, which is currently the largest food-focused social network. The recipes were selected from five categories: ‘Appetizers and Snacks’, ‘Breakfast and Lunch’, ‘Dessert’, ‘Dinner’ and ‘Drinks’. Semantic tags used for annotating food entities were selected from the Hansard corpus. To extract and annotate food entities, we applied a rule-based food NER method called FoodIE. Since FoodIE provides a weakly annotated corpus, by manually evaluating the obtained results on 1000 recipes, we created a gold standard of FoodBase. It consists of 12 844 food entity annotations describing 2105 unique food entities. Additionally, we provided a weakly annotated corpus on an additional 21 790 recipes. It consists of 274 053 food entity annotations, 13 079 of which are unique. The FoodBase corpus is necessary for developing corpus-based NER models for food science, as a new benchmark dataset for machine learning tasks such as multi-class classification, multi-label classification and hierarchical multi-label classification. FoodBase can be used for detecting semantic differences/similarities between food concepts, and after all we believe that it will open a new path for learning food embedding space that can be used in predictive studies.

The data used for analysis are becoming increasingly complex along several directions: high dimensionality, number of examples and availability of labels for the examples. This poses a variety of challenges for the existing machine learning methods, related to analyzing datasets with a large number of examples that are described in a high-dimensional space, where not all examples have labels provided. For example, when investigating the toxicity of chemical compounds, there are many compounds available that can be described with information-rich high-dimensional representations, but not all of the compounds have information on their toxicity. To address these challenges, we propose methods for semi-supervised learning (SSL) of feature rankings. The feature rankings are learned in the context of classification and regression, as well as in the context of structured output prediction (multi-label classification, MLC, hierarchical multi-label classification, HMLC and multi-target regression, MTR) tasks. This is the first work that treats the task of feature ranking uniformly across various tasks of semi-supervised structured output prediction. To the best of our knowledge, it is also the first work on SSL of feature rankings for the tasks of HMLC and MTR. More specifically, we propose two approaches—based on predictive clustering tree ensembles and the Relief family of algorithms—and evaluate their performance across 38 benchmark datasets. The extensive evaluation reveals that rankings based on Random Forest ensembles perform the best for classification tasks (incl. MLC and HMLC tasks) and are the fastest for all tasks, while ensembles based on extremely randomized trees work best for the regression tasks. Semi-supervised feature rankings outperform their supervised counterparts across the majority of datasets for all of the different tasks, showing the benefit of using unlabeled in addition to labeled data.

Due to technological advances, a massive amount of data is produced daily, presenting challenges for application areas where data needs to be labelled by a domain specialist or by expensive procedures, in order to be useful for supervised machine learning purposes. In order to select which data points will provide more information when labelled, one can make use of active learning methods. Active learning (AL) is a subfield of machine learning which addresses methods to build models with fewer, but more representative instances. Even though AL has been vastly studied, it has not been thoroughly investigated in hierarchical multi-label classification, a learning task where multiple class labels can be assigned to an instance and these labels are hierarchically structured. In this work, we provide a public framework containing baseline and state-of-the-art algorithms suitable for this task. Additionally, we also propose a new algorithm, namely Hierarchical Query-By-Committee (H-QBC), which is validated on datasets from different domains. Our results show that H-QBC is capable of providing superior predictive performance results compared to its competitors, while being computationally efficient and parameter free.

This paper describes the system proposed by the BIT-WOW team for NLPCC2022 shared task in Task5 Track1. The track is about multi-label towards abstracts of academic papers in scientific domain, which includes hierarchical dependencies among 1,530 labels. In order to distinguish semantic information among hierarchical label structures, we propose the Label-aware Graph Convolutional Network (LaGCN), which uses Graph Convolutional Network to capture the label association through context-based label embedding. Besides, curriculum learning is applied for domain adaptation and to mitigate the impact of a large number of categories. The experiments show that: 1) LaGCN effectively models the category information and makes a considerable improvement in dealing with a large number of categories; 2) Curriculum learning is beneficial for a single model in the complex task. Our best results were obtained by an ensemble model. According to the official results, our approach proved the best in this track.KeywordsHierarchical multi-label classificationGraph convolutional networkCurriculum learningLabel embedding

Condition-CNN: A hierarchical multi-label fashion image classification model