Binary Relevance Research Articles

An Improved Multi-label Classifier Chain Method for Automated Text Classification

Automated text classification is the task of grouping documents (text) automatically into categories from a predefined set. The conventional approach to classification involves mapping a single class label each to a data point (instance). In multi-label classification (MLC), the task is to develop models that could predict multiple class labels to a data instance. There exist several MLC methods such as classifier chain (CC) and binary relevance (BR). However, there are drawbacks with these methods such as random label sequence ordering issue. This study attempts to address this issue peculiar with the classifier chain method. In this paper, a hybrid heuristic evolutionary-based technique is proposed. The proposed PSOGCC is a combination of particle swarm optimization (PSO) and genetic algorithm (GA). Genetic operators of GA are integrated with the basic PSO algorithm for finding the global best solution representing an optimized label sequence order in the chain classifier. In the experiment, three MLC methods: BR, CC, and PSOGCC are implemented using five benchmark multi-label datasets and five standard evaluation metrics. The proposed PSOGCC method improved the predictive performance of the chain classifier by obtaining the best results of 98.66%, 99.5%, 99.16%, 99.33%, 0.0011 accuracy, precision, recall, f1 Score, and Hammingloss values, respectively.

Multi-Label Data Fusion to Support Agricultural Vulnerability Assessments

Identifying crop species and varieties adaptable to climate change impacts is one of the main aspects of climate vulnerability assessments. This estimation involves processing, integrating, and analyzing many information sources to provide accurate and timely responses. However, designing this evaluation, examine the information gathered, and reaching agreements among all stakeholders and experts, often requires considerable effort in time, money, and people. In this study, we propose a data fusion strategy to support climate vulnerability assessments by identifying the adaptability of crops in a territory in the short term. This strategy follows the Joint Directors of Laboratories’ data fusion model guidelines. It was evaluated and validated through a case study in Colombia’s upper Cauca river basin. For this purpose, we identified Climate, Soil, Water Quality, Productive Alliances, and Production as the most relevant data sources to be integrated, and using metrics such as Mean IR, SCUMBLE, TCS, among others, we evaluated the combined datasets according to their theoretical complexity. The adaptability of crops in a territory was addressed as a multi-label learning problem, assessing the performance of different multi-label classification and multi-view multi-label classification models with both test and actual data. Comparing the predicted crops with the actual ones, we obtained a 98% similarity without considering crop ranking using the Binary Relevance approach and the Random Forest and XGBoost algorithms. Using a more exhaustive test involving order, we obtained a maximum similarity of 67% applying Binary Relevance and Random Forest.

Student Performance Prediction with Optimum Multilabel Ensemble Model

Abstract One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.

Degradation State Partition and Compound Fault Diagnosis of Rolling Bearing Based on Personalized Multilabel Learning

The prognostic and health management (PHM) of rolling bearings has been a popular research area. Since bearing fault is inevitable during degradation, how to improve the PHM performance based on both degradation states and fault types is still an open problem. In this study, two multilabel learning algorithms are proposed for PHM of rolling bearings, named personalized binary relevance (PBR) and hierarchical multilabel K-nearest neighbor (HML-KNN), respectively. Degradation states and fault types are used as the labels of the bearing data so that each sample has a corresponding label sequence, that is to say, the PHM problem is converted to a multilabel learning problem. Both algorithms have a personalized search process, which can not only help samples build a personalized model to improve classification accuracy but also solve the problem of data imbalance between labels. At the same time, the two algorithms also have their own characteristics and focus on different application situations. The PBR algorithm has faster modeling speed, more flexible use, and replaceable subclassifiers. HML-KNN is a high-order algorithm with global information analysis capabilities through the hierarchical processing of data and the conversion of label information. Both methods have achieved good enough results in the XJTU-SY bearing dataset. In order to illustrate the practicality of the algorithm, the experimental part further increases the difficulty. Using only a single faulty sample as the training set to determine the type of compound fault, the algorithms also show high performance. In the era of industrial big data, the depth of data mining and the design of algorithm models will help us better manage equipment health.

“FRIENDLY CHAT”- A chat application with multi-headed classification models for identifying abusive levels and their comparative study

An application that tracks the use of offensive and vulgar language on chats on social media networks in order to prevent users from becoming disrespectful may play a role in changing how users interact. "Friendly chat" is a play on a community messaging program, which allows users to address a range of topics that are totally unregulated. Once things start heating up a user may become abusive towards other users, the application can disperse the situation quickly by implementing protective measures to stop the abusive user in his tracks. Abuse is however not the only type of "toxic" material to which a user is exposed when communicating with other users. Hatred of identification, intimidation, and other forms of obscenity may also become a part of the discussion. To identify a post as toxic, we need to refer to a reference, using the "toxic comment" dataset consisting of a large number of Wikipedia comments which have been rated by human critics who classify the comment to fall under one or more of the above-mentioned categories. Integrate this model in the real time data that we receive from “Friendly Chat” to classify a user into one or more of these categories. Use techniques of classification and machine learning such as Naïve Bayes, LSTM and Binary relevance and chain classifiers models. Our application’s capable of detecting abusive users.

Multi-label classification by formulating label-specific features from simultaneous instance level and feature level

Multi-label learning (MLL) trains a classification model from multiple labelled datasets, where each training instance is annotated with a set of class labels simultaneously. Following the binary relevance MLL paradigm, a recently effective spirit is to constructing specific features for each label, instead of training over the original feature space. Existing label-specific methods, however, only consider the information from instance distributions, making the reconstructed features poorly discriminative. In this paper, we propose the generation of Label-spEcific feaTures by simultaneously exploring insTance distributions and fEatuRe distributions, and suggest a new method named Letter. Letter reconstructs two subsets of new features from the instance level and feature level, respectively. More concretely, from the instance level, Letter incorporates a sparse constraint, and from the feature level, we cluster the original features to construct new features as an extension. The combination of these two new feature subsets is the final set of label-specific features. Extensive experiments on a total of 14 benchmark datasets verify the competitive performance of Letter against the existing state-of-the-art MLL methods.

Klasifikasi Multi Label pada Hadis Bukhari Terjemahan Bahasa Indonesia Menggunakan Mutual Information dan k-Nearest Neighbor

Hadith is the second source of law for Muslims after the Qur'an which comes from various forms of the words, actions and stipulations of the Prophet Muhammad or referred to as his sunnah. In order to make it easier for Muslims to apply the teachings of the hadiths, a classification system is needed that can categorize a hadith into a class or a combination of two of the three classes which called a multi-label classification. In building a text classification system, there are various classification techniques, one of which is k-Nearest Neighbor (KNN). KNN is a simple and effective classification method for text classification, but has a weakness in processing data with high vector dimensions so that the computation time is higher and the efficiency of text classification is very low. Mutual Information (MI) is used as a feature selection method to reduce vector dimensions because it has the ability to show how strong a feature is in making a correct prediction of a class. In this study Problem Transformation Method with the Binary Relevance (BR) approach is used so that the multi label classification process can be accomplished. The optimum results obtained in this study shows the value of hamming loss is 0.0886 or about 91.14% of data were correctly classified and computational time for 595 seconds by using MI as a feature selection, but without stemming.

IMPT-FRAKEL: A Simple Multi-label Web-server that Only Uses Fingerprints to Identify which Metabolic Pathway Types Compounds can Participate In

Background: Metabolic pathway is one of the most basic biological pathways in living organisms. It consists of a series of chemical reactions and provides the necessary molecules and energies for organisms. To date, lots of metabolic pathways have been detected. However, there still exist hidden participants (compounds and enzymes) for some metabolic pathways due to the complexity and diversity of metabolic pathways. It is necessary to develop quick, reliable, and non-animal-involved prediction model to recognize metabolic pathways for any compound. Methods: In this study, a multi-label classifier, namely iMPT-FRAKEL, was developed for identifying which metabolic pathway types that compounds can participate in. Compounds and 12 metabolic pathway types were retrieved from KEGG. Each compound was represented by its fingerprints, which was the most widely used form for representing compounds and can be extracted from its SMILES format. A popular multi-label classification scheme, Random k-Labelsets (RAKEL) algorithm, was adopted to build the classifier. Classic machine learning algorithm, Support Vector Machine (SVM) with RBF kernel, was selected as the basic classification algorithm. Ten-fold cross-validation was used to evaluate the performance of the iMPT-FRAKEL. In addition, a web-server version of such classifier was set up, which can be assessed at http://cie.shmtu.edu.cn/impt/index. Results: iMPT-FRAKEL yielded the accuracy of 0.804, exact match of 0.745 and hamming loss of 0.039. Comparison results indicated that such classifier was superior to other models, including models with Binary Relevance (BR) or other classification algorithms. Conclusion: The proposed classifier employed limited prior knowledge of compounds but gives satisfying performance for recognizing metabolic pathways of compounds.

An efficient stacking model with label selection for multi-label classification

Binary relevance (BR) is one of the most popular frameworks in multi-label learning. It constructs a group of binary classifiers, one for each label. BR is a simple and intuitive way to deal with multi-label problem, but fails to utilize label correlations. To deal with this problem, dependent binary relevance (DBR) and other works employ stacking learning paradigm for BR, in which all labels are viewed as additional features. Those works may be suboptimal as each label has its own most related label subset. In this paper, a novel two-layer stacking based approach, which is named a Stacking Model with Label Selection (SMLS), is induced to exploit proper label correlations for improving the performance of DBR. At the first layer, we construct several binary classifiers in the way of BR. At the second layer, we find the specific label subset through label selection for each labels , and expand them into feature space. The final binary classifiers are constructed based on their corresponding augmented feature space. Comprehensive experiments are conducted on a collection of benchmark data sets. Comparison results with the state-of-the-art approaches validate the competitive performance of our proposed approach. Comparison results with DBR shows that our approach is not only more time efficient but also more robust.

Diagnosis of complications of type 2 diabetes based on weighted multi-label small sphere and large margin machine

At present, type 2 diabetes mellitus (T2DM) is one of the most serious and critical health problems. Persistent hyperglycemia of diabetic patients can lead to other complications, such as macrovascular, microvascular, neuropathy, which are the main cause of death in diabetic patients. Therefore, it is an urgent task to diagnose the complications. To address the above issue, we turn it into a multi-label classification problem by taking macrovascular, microvascular, neuropathy as three labels. Furthermore, we find that it is an imbalanced classification problem for each label. Thus, a novel weighted multi-label small sphere and large margin machine (WML-SSLM) is proposed to diagnose the complications from T2DM in this paper, which is constructed by introducing the binary relevance (BR) method to SSLM. Compared with the BR method, WML-SSLM considers the relevance of labels by giving different weights for different instances. Taking the diabetes dataset from the Chinese PLA General Hospital as the research object, the diagnosis of the macrovascular, microvascular, and neuropathy from T2DM are studied by using our proposed WML-SSLM. The experimental results show that WML-SSLM can effectively deal with the prediction of complications of T2DM. Besides, the relevant features of each complication are analyzed by using the student’s t-test.

A generalized weighted distance k-Nearest Neighbor for multi-label problems

In multi-label classification, each instance is associated with a set of pre-specified labels. One common approach is to use Binary Relevance (BR) paradigm to learn each label by a base classifier separately. Use of k-Nearest Neighbor (kNN) as the base classifier (denoted as BRkNN) is a simple, descriptive and powerful approach. In binary relevance a highly imbalanced view of dataset is used. However, kNN is known to perform poorly on imbalanced data. One approach to deal with this is to define the distance function in a parametric form and use the training data to adjust the parameters (i.e. adjusting boundaries between classes) by optimizing a performance measure customized for imbalanced data e.g. F-measure. Prototype Weighting (PW) scheme presented in the literature (Paredes & Vidal, 2006) uses gradient descent to specify the parameters by minimizing the classification error-rate on training data. This paper presents a generalized version of PW. First, instead of minimizing the error-rate proposed in PW, the generalized PW supports also other objective functions that use elements of confusion matrix (including F-measure). Second, PW originally presented for 1NN is extended to the general case of kNN (i.e., k>=1). For problems having highly overlapped classes, it is expected to perform better since a value of k>1 produces smoother decision boundaries which in turn can improve generalization. In multi-label problems with many labels or problems with highly overlapped classes, the proposed generalized PW is expected to significantly improve the performance as it involves many decision boundaries. The performance of the proposed method has been compared with state-of-the-art methods in multi-label classification containing 6 lazy classifiers based on kNN. Experiments show that the proposed method significantly outperforms other methods.

A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data

There are few studies in the literature to address the multi-objective multi-label feature selection for the classification of video data using evolutionary algorithms. Selecting the most appropriate subset of features is a significant problem while maintaining/improving the accuracy of the prediction results. This study proposes a framework of parallel multi-objective Non-dominated Sorting Genetic Algorithms (NSGA-II) for exploring a Pareto set of non-dominated solutions. The subsets of non-dominated features are extracted and validated by multi-label classification techniques, Binary Relevance (BR), Classifier Chains (CC), Pruned Sets (PS), and Random k-Labelset (RAkEL). Base classifiers such as Support Vector Machines (SVM), J48-Decision Tree (J48), and Logistic Regression (LR) are performed in the classification phase of the algorithms. Comprehensive experiments are carried out with local feature descriptors extracted from two multi-label data sets, the well-known MIR-Flickr dataset and a Wireless Multimedia Sensor (WMS) dataset that we have generated from our video recordings. The prediction accuracy levels are improved by 6.36% and 25.7% for the MIR-Flickr and WMS datasets respectively while the number of features is significantly reduced. The results verify that the algorithms presented in this new framework outperform the state-of-the-art algorithms.

Multi-label classification of line chart images using convolutional neural networks

In this paper, we propose a new convolutional neural network (CNN) architecture to build a multi-label classifier that categorizes line chart images according to their characteristics. The class labels are organized in the form of trend property (increasing or decreasing) and functional property (linear or exponential). In the proposed method, the Canny edge detection technique is applied as a data preprocessing step to increase both the classification accuracy and training speed. In addition, two different multi-label solution approaches are compared: label powerset (LP) and binary relevance (BR) methods. The experimental studies show that the proposed LP-CNN model achieves 93.75% accuracy, while the BR-CNN model reaches 92.97% accuracy on the test set, which contains real-world line chart images. The aim of this study is to build an efficient classifier that can be used for many purposes, such as automatically captioning the chart images, providing recommendations, redesigning charts, organizing a collection of chart images and developing better search engines.

Binary-Relevance Classification of Depression and Anxiety in the Elderly Using Low-Cost Activity Trackers

Understanding mood disorders of depression and anxiety in the elderly is important because those are common symptoms of dementia. This paper shows our methodology of supervised learning to understand the relationship between the diagnosis of depression and/or anxiety of the elderly and their activity patterns. Using low-cost activity trackers, activity data of the elderly were collected over several weeks per subject. We extracted features from the time-series activity data based on circadian rhythms. We have applied binary relevance to this problem of multi-label nature diagnosed with depression, anxiety or both. We tried various classification algorithms for each label and compared the performances to find the best classifier among the algorithms. The result yielded that the best classifier was the F1 score of 77.4% in the classification. Our study has yielded a meaningful result in screening mood disorders of the elderly using low-cost activity trackers.

Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning.

We present Peax, a novel feature-based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the user’s perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of the sequential data, our accompanying visual query system enables interactive feedback-driven adjustments of the pattern search to adapt to the users’ perceived similarity. Using an active learning sampling strategy, Peax collects user-generated binary relevance feedback. This feedback is used to train a model for binary classification, to ultimately find other regions that exhibit patterns similar to the search target. We demonstrate Peax’s features through a case study in genomics and report on a user study with eight domain experts to assess the usability and usefulness of Peax. Moreover, we evaluate the effectiveness of the learned feature representation for visual similarity search in two additional user studies. We find that our models retrieve significantly more similar patterns than other commonly used techniques.

Combining instance and feature neighbours for extreme multi-label classification

Extreme multi-label classification problems occur in different applications such as prediction of tags or advertisements. We propose a new algorithm that predicts labels using a linear ensemble of labels from instance- and feature-based nearest neighbours. In the feature-based nearest neighbours method, we precompute a matrix containing the similarities between each feature and label. For the instance-based nearest neighbourhood, we create an algorithm that uses an inverted index to compute cosine similarity on sparse datasets efficiently. We extend this baseline with a new top-k query algorithm that combines term-at-a-time and document-at-a-time traversal with tighter pruning based on a partition of the dataset. On ten real-world datasets, we find that our method outperforms state-of-the-art methods such as multi-label k-nearest neighbours, instance-based logistic regression, binary relevance with support vector machines and FastXml on different evaluation metrics. We also find that our algorithm is orders of magnitude faster than these baseline algorithms on sparse datasets and requires less than 20 ms per instance to predict labels for extreme datasets without the need for expensive hardware.

Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks

Crop diseases have always been a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automatic recognition and severity estimation of crop diseases on leaves plays a crucial role in agricultural sector. In this paper, we propose a series of automatic image-based crop leaf diseases recognition and severity estimation networks, i.e., BR-CNNs, which can simultaneously recognize crop species, classify crop diseases and estimate crop diseases severity based on deep learning. BR-CNNs based on binary relevance (BR) multi-label learning algorithm and deep convolutional neural network (CNN) approaches succeed in identifying 7 crop species, 10 crop diseases types (including Healthy) and 3 crop diseases severity kinds (normal, general and serious). Compared with LP-CNNs and MLP-CNNs, the overall performance of BR-CNNs is superior. The BR-CNN based on ResNet50 achieves the best test accuracy of 86.70%, which demonstrates the feasibility and effectiveness of our network. The BR-CNN based on the light-weight NasNet also achieves excellent test accuracy of 85.28%, which can provide more possibilities for the development of mobile systems and devices.

Klasifikasi Topik Multi Label pada Hadis Shahih Bukhari Menggunakan K-Nearest Neighbor dan Latent Semantic Analysis

Hadith is the second source of Islamic law after Al-Quran, making it important to study. However, there are some difficulties in learning hadith, such as to determine which hadith belongs to the topic of suggestions, prohibitions, and information. This certainly obstructs the hadith learning process, especially for Muslims. Therefore, it is necessary to classify hadiths into the topic of suggestions, prohibitions, information, and a combination of the three topics which also called as multi-label topic. The classification can be done with the K-Nearest Neighbor, it is one of the best methods in the Vector Space Model and is the simplest but quite effective method. However, the KNN has a lack in dealing with high vector dimension, resulting in the long time computing classification. For that reason, it is necessary to classify Sahih Bukhari's Hadiths into the topic of recommendations, prohibitions, and information using the Latent-Semantic Analysis - K-nearest Neighbor (LSA-KNN) method. Binary Relevance method is also employed in this research to process the multi-label data. This research shows that the performance of LSA-KNN is 90.28% with the computation time is 19 minutes 21 seconds and the performance of KNN is 90.23% with the computation time is 37 minutes 06 seconds, which means that the LSA-KNN method has a better performance than KNN

Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions.

Structural information of materials such as the crystal systems and space groups are highly useful for analyzing their physical properties. However, the enormous composition space of materials makes experimental X-ray diffraction (XRD) or first-principle-based structure determination methods infeasible for large-scale material screening in the composition space. Herein, we propose and evaluate machine-learning algorithms for determining the structure type of materials, given only their compositions. We couple random forest (RF) and multiple layer perceptron (MLP) neural network models with three types of features: Magpie, atom vector, and one-hot encoding (atom frequency) for the crystal system and space group prediction of materials. Four types of models for predicting crystal systems and space groups are proposed, trained, and evaluated including one-versus-all binary classifiers, multiclass classifiers, polymorphism predictors, and multilabel classifiers. The synthetic minority over-sampling technique (SMOTE) is conducted to mitigate the effects of imbalanced data sets. Our results demonstrate that RF with Magpie features generally outperforms other algorithms for binary and multiclass prediction of crystal systems and space groups, while MLP with atom frequency features is the best one for structural polymorphism prediction. For multilabel prediction, MLP with atom frequency and binary relevance with Magpie models are the best for predicting crystal systems and space groups, respectively. Our analysis of the related descriptors identifies a few key contributing features for structural-type prediction such as electronegativity, covalent radius, and Mendeleev number. Our work thus paves a way for fast composition-based structural screening of inorganic materials via predicted material structural properties.

Comparison of base classifiers for multi-label learning

Multi-label learning methods can be categorised into algorithm adaptation, problem transformation and ensemble methods. Some of these methods depend on a base classifier and the relationship is not well understood. In this paper the sensitivity of five problem transformation and two ensemble methods to four types of classifiers is studied. Their performance across 11 benchmark datasets is measured using 16 evaluation metrics. The best classifier is shown to depend on the method: Support Vector Machines (SVM) for binary relevance, classifier chains, calibrated label ranking, quick weighted multi-label learning and RAndom k-labELsets; k-Nearest Neighbours (k-NN) and Naïve Bayes (NB) for Hierarchy Of Multilabel classifiERs; and Decision Trees (DT) for ensemble of classifier chains. The statistical performance of a classifier is also found to be generally consistent across the metrics for any given method. Overall, DT and SVM have the best performance–computational time trade-off followed by k-NN and NB.