Multi-label Classification Approach Research Articles

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

AbstractRapid identification of infrastructure disruptions during a disaster plays an important role in restoration and recovery operations. Due to the limitations of using physical sensing technologies, such as the requirement to cover a large area in a short period of time, studies have investigated the potential of social sensing for damage/disruption assessment following a disaster. However, previous studies focused on identifying whether a social media post is damage related or not. Hence, advanced methods are needed to infer actual infrastructure disruptions and their locations from such data. In this paper, we present a multilabel classification approach to identify the co‐occurrence of multiple types of infrastructure disruptions considering the sentiment toward a disruption—whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). In addition, we propose a dynamic mapping framework for visualizing infrastructure disruptions. We use a geo‐parsing method that extracts location from the texts of a social media post. We test the proposed approach using Twitter data collected during hurricanes Irma and Michael. The proposed multilabel classification approach performs better than a baseline method (using simple keyword search and sentiment analysis). We also find that disruption‐related tweets, based on specific keywords, do not necessarily indicate an actual disruption. Many tweets represent general conversations, concerns about a potential disruption, and positive emotion for not being affected by any disruption. In addition, a dynamic disruption map has potential in showing county and point/coordinate level disruptions. Identifying disruption types and their locations is vital for disaster recovery, response, and relief actions. By inferring the co‐occurrence of multiple disruptions, the proposed approach may help coordinate among infrastructure service providers and disaster management organizations.

Hierarchical multilabel classifier for gene ontology annotation using multihead and multiend deep CNN model

AbstractGene ontology annotation is known to be a very complicated multilabel classification task, and the hierarchical multilabel classification (HMC) approaches with local classifiers have been shown to be effective for the task. In a traditional HMC method, a set of hierarchically organized simple local classifiers are usually used, each of which for one hierarchical level separately. In this paper, we propose a novel hierarchical multilabel classifier implementing the whole set of hierarchically organized local classifiers in one deep convolution neural network (CNN) model with multiple heads and multiple ends (MHME). The proposed MHME CNN model consists of three parts: the body part of a deep CNN model shared by different local classifiers for feature extraction and feature mapping; the multiend part of a set of autoencoders performing feature fusion transforming the input vectors of different local classifiers to feature vectors with the same length so as to share the feature mapping part; and the multihead part of a set of linear multilabel classifiers. Furthermore, a sophisticated recursive algorithm is designed to train the MHME CNN model to realize the functions of a set of hierarchically organized local classifiers. In this way, by sharing a deep CNN with multiple local classifiers, we are able to construct more powerful local classifiers for each level with limited training samples, and to achieve better classification performance. Experiment results on various benchmark datasets show that the proposed deep CNN‐based model has better performance than the state‐of‐the‐art traditional models. Moreover, it gives rather good performance even under a transfer learning. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Comprehensive study and Analysis of Extreme Multi-Label Classification Approach

Comprehensive study and Analysis of Extreme Multi-Label Classification Approach

Code smell detection using multi-label classification approach

Code smells are characteristics of the software that indicates a code or design problem which can make software hard to understand, evolve, and maintain. There are several code smell detection tools proposed in the literature, but they produce different results. This is because smells are informally defined or subjective in nature. Machine learning techniques help in addressing the issues of subjectivity, which can learn and distinguish the characteristics of smelly and non-smelly source code elements (classes or methods). However, the existing machine learning techniques can only detect a single type of smell in the code element that does not correspond to a real-world scenario as a single element can have multiple design problems (smells). Further, the mechanisms proposed in the literature could not detect code smells by considering the correlation (co-occurrence) among them. To address these shortcomings, we propose and investigate the use of multi-label classification (MLC) methods to detect whether the given code element is affected by multiple smells or not. In this proposal, two code smell datasets available in the literature are converted into a multi-label dataset (MLD). In the MLD, we found that there is a positive correlation between the two smells (long method and feature envy). In the classification phase, the two methods of MLC considered the correlation among the smells and enhanced the performance (on average more than 95% accuracy) for the 10-fold cross-validation with the ten iterations. The findings reported help the researchers and developers in prioritizing the critical code elements for refactoring based on the number of code smells detected.

Using different machine learning approaches to evaluate performance on spare parts request for aircraft engines

The Aircraft uptime is getting increasingly important as the transport solutions become more complex and the transport industry seeks new ways of being competitive. To reach this objective, traditional Fleet Management systems are gradually extended with new features to improve reliability and then provide better maintenance planning. Main goal of this work is the development of iterative algorithms based on Artificial Intelligence to define the engine removal plan and its maintenance work, optimizing engine availability at the customer and maintenance costs, as well as obtaining a procurement plan of integrated parts with planning of interventions and implementation of a maintenance strategy. In order to reach this goal, Machine Learning has been applied on a workshop dataset with the aim to optimize warehouse spare parts number, costs and lead-time. This dataset consists of the repair history of a specific engine type, from several years and several fleets, and contains information like repair claim, engine working time, forensic evidences and general information about processed spare parts. Using these data as input, several Machine Learning models have been built in order to predict the repair state of each spare part for a better warehouse handling. A multi-label classification approach has been used in order to build and train, for each spare part, a Machine Learning model that predicts the part repair state as a multiclass classifier does. Mainly, each classifier is requested to predict the repair state (classified as “Efficient”, “Repaired” or “Replaced”) of the corresponding part, starting from two variables: the repairing claim and the engine working time. Then, global results have been evaluated using the Confusion Matrix, from which Accuracy, Precision, Recall and F1-Score metrics are retrieved, in order to analyse the cost of incorrect prediction. These metrics are calculated for each spare part related model on test sets and, then, a final single performance value is obtained by averaging results. In this way, three Machine Learning models (Naïve Bayes, Logistic Regression and Random Forest classifiers) are applied and results are compared. Naïve Bayes and Logistic Regression, that are fully probabilistic methods, have best global performances with an accuracy value of almost 80%, making the models being correct most of the times.

Multi-ECGNet for ECG Arrythmia Multi-Label Classification

With the development of various deep learning algorithms, the importance and potential of AI + medical treatment are increasingly prominent. Electrocardiogram (ECG) as a common auxiliary diagnostic index of heart diseases, has been widely applied in the pre-screening and physical examination of heart diseases due to its low price and non-invasive characteristics. Currently, the multi-lead ECG equipments have been used in the clinic, and some of them have the automatic analysis and diagnosis functions. However, the automatic analysis is not accurate enough for the discrimination of abnormal events of ECG, which needs to be further checked by doctors. We therefore develop a deep-learning-based approach for multi-label classification of ECG named Multi-ECGNet, which can effectively identify patients with multiple heart diseases at the same time. The experimental results show that the performance of our methods can get a high score of 0.863 (micro-F1-score) in classifying 55 kinds of arrythmias, which is beyond the level of ordinary human experts.

Classify multi-label images via improved CNN model with adversarial network

Convolution neural network (CNN) achieves outstanding results in single-label image classification task. However, due to the complex underlying object layout and insufficient multi-label training images, how to achieve better performance for multi-label images via CNN is still an open problem. In this work, we propose an improved deep CNN model which can extract features of objects at different scales in multi-label images by spatial pyramid pooling as well as feature fusion. In model training, we first transfer the parameters pre-trained on ImageNet to our model, then an Adversarial Network is trained to generate examples with occlusions, which makes our model invariant to occlusions. Experimental results on Pascal VOC 2012 and Corel 5K image datasets demonstrate the superiority of the proposed approach over many approaches. The mAP of our model reaches 84.0% on the VOC 2012 dataset, which significantly outperforms most approaches and closes to HCP, the representative multi-label classification approach.

User Association for Ultra-Dense mmWave Networks With Multi-Connectivity: A Multi-Label Classification Approach

Millimeter wave (mmWave) communication has been considered as a promising means to fulfill the requirements of next generation cellular networks. To conquer the limited coverage and the severe penetration attenuation, we investigate the ultra-dense mmWave networks with multi-connectivity in this letter, which enables users to access multiple mmWave base stations simultaneously. A user association problem is formulated to maximize the system throughput, which is NP-hard. To tackle it, we introduce a multi-label classification approach to solve the proposed combinatorial problem. Numerical results demonstrate the good optimality and low computational complexity of our proposed algorithms.

ML2S-SVM: multi-label least-squares support vector machine classifiers

PurposeImage classification is becoming a supporting technology in several image-processing tasks. Due to rich semantic information contained in the images, it is very popular for an image to have several labels or tags. This paper aims to develop a novel multi-label classification approach with superior performance.Design/methodology/approachMany multi-label classification problems share two main characteristics: label correlations and label imbalance. However, most of current methods are devoted to either model label relationship or to only deal with unbalanced problem with traditional single-label methods. In this paper, multi-label classification problem is regarded as an unbalanced multi-task learning problem. Multi-task least-squares support vector machine (MTLS-SVM) is generalized for this problem, renamed as multi-label LS-SVM (ML2S-SVM).FindingsExperimental results on the emotions, scene, yeast and bibtex data sets indicate that the ML2S-SVM is competitive with respect to the state-of-the-art methods in terms of Hamming loss and instance-based F1 score. The values of resulting parameters largely influence the performance of ML2S-SVM, so it is necessary for users to identify proper parameters in advance.Originality/valueOn the basis of MTLS-SVM, a novel multi-label classification approach, ML2S-SVM, is put forward. This method can overcome the unbalanced problem but also explicitly models arbitrary order correlations among labels by allowing multiple labels to share a subspace. In addition, the multi-label classification approach has a wider range of applications. That is to say, it is not limited to the field of image classification.

STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity.

Membrane transport proteins play crucial roles in the pharmacokinetics of substrate drugs, the drug resistance in cancer and are vital to the process of drug discovery, development and anti-cancer therapeutics. However, experimental methods to profile a substrate drug against a panel of transporters to determine its specificity are labor intensive and time consuming. In this article, we aim to develop an in silico multi-label classification approach to predict whether a substrate can specifically recognize one of the 13 categories of drug transporters ranging from ATP-binding cassette to solute carrier families using both structural fingerprints and chemical ontologies information of substrates. The data-driven network-based label space partition (NLSP) method was utilized to construct the model based on a hybrid of similarity-based feature by the integration of 2D fingerprint and semantic similarity. This method builds predictors for each label cluster (possibly intersecting) detected by community detection algorithms and takes union of label sets for a compound as final prediction. NLSP lies into the ensembles of multi-label classifier category in multi-label learning field. We utilized Cramér's V statistics to quantify the label correlations and depicted them via a heatmap. The jackknife tests and iterative stratification based cross-validation method were adopted on a benchmark dataset to evaluate the prediction performance of the proposed models both in multi-label and label-wise manner. Compared with other powerful multi-label methods, ML-kNN, MTSVM, and RAkELd, our multi-label classification model of NLPS-RF (random forest-based NLSP) has proven to be a feasible and effective model, and performed satisfactorily in the predictive task of transporter-substrate specificity. The idea behind NLSP method is intriguing and the power of NLSP remains to be explored for the multi-label learning problems in bioinformatics. The benchmark dataset, intermediate results and python code which can fully reproduce our experiments and results are available at https://github.com/dqwei-lab/STS.

Correlated Multi-label Classification with Incomplete Label Space and Class Imbalance

Multi-label classification is defined as the problem of identifying the multiple labels or categories of new observations based on labeled training data. Multi-labeled data has several challenges, including class imbalance, label correlation, incomplete multi-label matrices, and noisy and irrelevant features. In this article, we propose an integrated multi-label classification approach with incomplete label space and class imbalance (ML-CIB) for simultaneously training the multi-label classification model and addressing the aforementioned challenges. The model learns a new label matrix and captures new label correlations, because it is difficult to find a complete label vector for each instance in real-world data. We also propose a label regularization to handle the imbalanced multi-labeled issue in the new label, and l 1 regularization norm is incorporated in the objective function to select the relevant sparse features. A multi-label feature selection (ML-CIB-FS) method is presented as a variant of the proposed ML-CIB to show the efficacy of the proposed method in selecting the relevant features. ML-CIB is formulated as a constrained objective function. We use the accelerated proximal gradient method to solve the proposed optimisation problem. Last, extensive experiments are conducted on 19 regular-scale and large-scale imbalanced multi-labeled datasets. The promising results show that our method significantly outperforms the state-of-the-art.

A multilabel classification approach for complex human activities using a combination of emerging patterns and fuzzy sets

In our daily lives, humans perform different Activities of Daily Living (ADL), such as cooking, and studying. According to the nature of humans, they perform these activities in a sequential/simple or an overlapping/complex scenario. Many research attempts addressed simple activity recognition, but complex activity recognition is still a challenging issue. Recognition of complex activities is a multilabel classification problem, such that a test instance is assigned to a multiple overlapping activities. Existing data-driven techniques for complex activity recognition can recognize a maximum number of two overlapping activities and require a training dataset of complex (i.e. multilabel) activities. In this paper, we propose a multilabel classification approach for complex activity recognition using a combination of Emerging Patterns and Fuzzy Sets. In our approach, we require a training dataset of only simple (i.e. single-label) activities. First, we use a pattern mining technique to extract discriminative features called Strong Jumping Emerging Patterns (SJEPs) that exclusively represent each activity. Then, our scoring function takes SJEPs and fuzzy membership values of incoming sensor data and outputs the activity label(s). We validate our approach using two different dataset. Experimental results demonstrate the efficiency and superiority of our approach against other approaches.

Mining non-functional requirements from App store reviews

User reviews obtained from mobile application (app) stores contain technical feedback that can be useful for app developers. Recent research has been focused on mining and categorizing such feedback into actionable software maintenance requests, such as bug reports and functional feature requests. However, little attention has been paid to extracting and synthesizing the Non-Functional Requirements (NFRs) expressed in these reviews. NFRs describe a set of high-level quality constraints that a software system should exhibit (e.g., security, performance, usability, and dependability). Meeting these requirements is a key factor for achieving user satisfaction, and ultimately, surviving in the app market. To bridge this gap, in this paper, we present a two-phase study aimed at mining NFRs from user reviews available on mobile app stores. In the first phase, we conduct a qualitative analysis using a dataset of 6,000 user reviews, sampled from a broad range of iOS app categories. Our results show that 40% of the reviews in our dataset signify at least one type of NFRs. The results also show that users in different app categories tend to raise different types of NFRs. In the second phase, we devise an optimized dictionary-based multi-label classification approach to automatically capture NFRs in user reviews. Evaluating the proposed approach over a dataset of 1,100 reviews, sampled from a set of iOS and Android apps, shows that it achieves an average precision of 70% (range [66% - 80%]) and average recall of 86% (range [69% - 98%]).

Evaluating multi-label classifiers and recommender systems in the financial service sector

The objective of this paper is to evaluate multi-label classification techniques and recommender systems for cross-sell purposes in the financial services sector. We carried out three analyses using data obtained from an international financial services provider. First, we tested four multi-label classification techniques, of which the two problem transformation methods were combined with several base classifiers. Second, we benchmarked the performance of five state-of-the-art recommender approaches. Third, we compared the best performing multi-label classification and recommender approaches with each other. The results identify user-based collaborative filtering as the top performing recommender system, with a cross-validated F1 measure of 42.20% and G-mean of 42.64%. Classifier chains binary relevance with adaboost and binary relevance with random forest are the top performing multi-label classification algorithms for respectively F1 measure and G-mean, yielding a cross-validated F1 measure of 53.33% and G-mean of 54.37%. The statistical comparison between the best performing approaches confirms the superiority of multi-label classification techniques. Our study provides important recommendations for financial services providers, who are interested in the most effective methods to determine cross-sell opportunities. In previous studies, multi-label classification techniques and recommender systems were always investigated independently of each other. To the best of our knowledge, our study is therefore the first to compare both techniques in the financial services sector.

Extreme multi-label learning : A large scale classification approach in machine learning

In digital world, the amount of data is growing exponentially in day to day life. It is difficult to analyze and extract knowledge from large amount of data with millions of categories in Big Data environment. Therefore, it is challenging problem to develop model that classify large volume of documents available on Internet. However, Multi-Label Classification approach is used to classify data with multiple categories or labels but it is inefficient way to deal with millions of categories. Hence Extreme Multi-Label Classification approach is used to overcome this limitation by selecting subset of labels for the new instance from millions of labels. Recently Extreme Multi-Label Classification has attracted research attention in different application areas like document categorization in Wikipedia, people identification in social networking, gene prediction in bio-informatics etc. Extreme Multi-Label Classification is also opened up new challenge to reformulate existing machine learning problems like ranking, tagging and recommendation. This survey paper focuses on approaches and reviewing current research challenges on eXtreme Multi Label Classification. Also discussed state-of-the-art algorithms to handle eXtreme Multi-Label Classification Problem.

Graph convolutional network for multi-label VHR remote sensing scene recognition

We address the problem of multi-label scene classification from Very High Resolution (VHR) satellite remote sensing (RS) images in this paper by exploring the deep graph convolutional network (GCN). Since a given VHR RS scene contains several local features, the traditional single-label classification frameworks do not convey the true semantics of the scene. The multi-label classification approaches, on the other hand, is expected to aid in better characterization of the area under consideration. Under the multi-label setup, we find it intuitive to represent a given image as a region adjacency graph (RAG) of the respective local regions. In order to extract discriminative features from such irregular structures for enhanced classification, we model the subsequent supervised learning problem in terms of a novel multi-label deep GCN. We validate the efficacy of the proposed technique on the popular UC-Merced dataset where our framework outperforms with respect to the literature.

A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets

Currently, people use online social media such as Twitter or Facebook to share their emotions and thoughts. Detecting and analyzing the emotions expressed in social media content benefits many applications in commerce, public health, social welfare, etc. Most previous work on sentiment and emotion analysis has only focused on single-label classification and ignored the co-existence of multiple emotion labels in one instance. This paper describes the development of a novel deep learning-based system that addresses the multiple emotion classification problem in Twitter. We propose a novel method to transform it to a binary classification problem and exploit a deep learning approach to solve the transformed problem. Our system outperforms the state-of-the-art systems, achieving an accuracy score of 0.59 on the challenging SemEval2018 Task 1:E-cmulti-label emotion classification problem.

Contributions of MIR to soundscape ecology. Part 3: Tagging and classifying audio features using a multi-labeling k-nearest neighbor approach

Scientists are using acoustic monitoring to assess the impact of altered soundscapes on wildlife communities and human systems. In the soundscape ecology field, monitoring and analyses approaches rely on the interdisciplinary intersection of ecology, acoustics, and computer science. Combining theory and practice of each field in the context of Knowledge Discovery in Databases (KDD), soundscape ecologists provide innovative monitoring solutions for ecologically-driven research questions. We propose a soundscape content analysis framework for improved knowledge outcome with assistance of the new multi-label (ML) concept.Here, we investigated the effectiveness of a ML k-nearest neighbor algorithm (ML-kNN) for labeling concurrent soundscape components within a single recording. We manually labeled 1200 field recordings for the presence of soundscape components and extracted ecological acoustic features, audio profile features, and Gaussian-mixture model features for each recording. Then, we tested the ML-kNN algorithm accuracy with well-established metrics adapted to ML learning.We found that seventeen unique acoustic features could predict a set of biophonic, geophonic, and anthrophonic labels for a single field recording with average precision of 0.767. However, certain labels were predicted incorrectly depending on the time of day and co-occurrence of that label with another label, suggesting further refinement is needed to improve the accuracy of predicted labels.Overall, this ML classification approach could enable researchers to label field recordings more quickly and generate an “alert” system for monitoring changes in a specific sound class. Ultimately, the adaptation of the ML algorithm may provide soundscape ecologists with new metadata labels that are searchable in large databases of soundscape field recordings.

Classifying Biomedical Figures by Modality via Multi-Label Learning.

The figures found in biomedical literature are a vital part of biomedical research, education, and clinical decision. The multitude of their modalities and the lack of corresponding metadata constitute search and information, retrieval a difficult task. In this paper, we introduce novel multi-label modality classification approaches for biomedical figures without segmenting the compound figures. In particular, we investigate using both simple and compound figures for training a multi-label model to be used for annotating either all figures or only those predicted as compound by a compound figure detection model. Using data from the medical task of ImageCLEF 2016, we train our approaches with visual features and compare them with the approach involving compound figure separation into sub-figures. Furthermore, we study how multimodal learning, from both visual and textual features affects the tasks of classifying biomedical figures by modality and detecting compound figures. Finally, we present a web application for medical figure retrieval, which is based on one of our classification approaches and allows users to search for figures of PubMed Central from any device and provide feedback about the modality of a figure classified by the system.

A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases

Chest X-ray (CXR) is one of the most common types of radiology examination for the diagnosis of thorax diseases. Computer-aided diagnosis (CAD) was developed to help radiologists to achieve diagnostic excellence in a short period of time and to enhance patient healthcare. In this paper, we seek to improve the performance of the CAD system in the task of thorax diseases diagnosis by providing a new method that combines the advantages of CNN models in image feature extraction with those of the problem transformation methods in the multi-label classification task. The experimental study is tested on two publicly available CXR datasets ChestX-ray14 (frontal view) and CheXpert (frontal and lateral views). The results show that our proposed method outperformed the current state of the art.