Label Powerset Research Articles

Antibody Class(es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm.

Development of vaccines and therapeutic antibodies to deal with infectious and other diseases are the most perceptible scientific interventions that have had huge impact on public health including that in the current Covid-19 pandemic. From inactivation methodologies to reverse vaccinology, vaccine development strategies of 21st century have undergone several transformations and are moving towards rational design approaches. These developments are driven by data as the combinatorials involved in antigenic diversity of pathogens and immune repertoire of hosts are enormous. The computational prediction of epitopes is central to these developments and numerous B-cell epitope prediction methods developed over the years in the field of immunoinformatics have contributed enormously. Most of these methods predict epitopes that could potentially bind to an antibody regardless of its type and only a few account for antibody class specific epitope prediction. Recent studies have provided evidence of more than one class of antibodies being associated with a particular disease. Therefore, it is desirable to predict and prioritize ‘peptidome’ representing B-cell epitopes that can potentially bind to multiple classes of antibodies, as an open problem in immunoinformatics. To address this, AbCPE, a novel algorithm based on multi-label classification approach has been developed for prediction of antibody class(es) to which an epitope can potentially bind. The epitopes binding to one or more antibody classes (IgG, IgE, IgA and IgM) have been used as a knowledgebase to derive features for prediction. Multi-label algorithms, Binary Relevance and Label Powerset were applied along with Random Forest and AdaBoost. Classifier performance was assessed using evaluation measures like Hamming Loss, Precision, Recall and F1 score. The Binary Relevance model based on dipeptide composition, Random Forest and AdaBoost achieved the best results with Hamming Loss of 0.1121 and 0.1074 on training and test sets respectively. The results obtained by AbCPE are promising. To the best of our knowledge, this is the first multi-label method developed for prediction of antibody class(es) for sequential B-cell epitopes and is expected to bring a paradigm shift in the field of immunoinformatics and immunotherapeutic developments in synthetic biology. The AbCPE web server is available at http://bioinfo.unipune.ac.in/AbCPE/Home.html.

Harnessing Multi-label Classification Approaches for Economic Phenomena Categorization

One fashion to report a country’s economic state is by compiling economic phenomena from several sources. The collected data may be explored based on their sentiments and economic categories. This research attempted to perform and analyze multiple approaches to multi-label text classification in addition to providing sentiment analysis on the economic phenomena. The sentiment and single-label category classification was performed utilizing the logistic regression model. Meanwhile, the multi-label category classification was fulfilled using a combination of logistic regression, support vector machines, k-nearest neighbor, naïve Bayes, and decision trees as base classifiers, with binary relevance, classifier chain, and label power set as the implementation approaches. The results showed that logistic regression works well in sentiment and single-label classification, with a classification accuracy of 80.08% and 92.71%, respectively. However, it was also discovered that it works poorly as a base classifier in multi-label classification, indicated by the classification accuracy dropping to 13.35%, 15.40%, and 30.65% for binary relevance, classifier chain, and label power set, respectively. Alternatively, naïve Bayes works best as a base classifier in the label power set approach for multi-label classification, with a classification accuracy of 63.22%, followed by decision trees and support vector machines.

Well-calibrated confidence measures for multi-label text classification with a large number of labels

We extend our previous work on Inductive Conformal Prediction (ICP) for multi-label text classification and present a novel approach for addressing the computational inefficiency of the Label Powerset (LP) ICP, arrising when dealing with a high number of unique labels. We present experimental results using the original and the proposed efficient LP-ICP on two English and one Czech language data-sets. Specifically, we apply the LP-ICP on three deep Artificial Neural Network (ANN) classifiers of two types: one based on contextualised (bert) and two on non-contextualised (word2vec) word-embeddings. In the LP-ICP setting we assign nonconformity scores to label-sets from which the corresponding p-values and prediction-sets are determined. Our approach deals with the increased computational burden of LP by eliminating from consideration a significant number of label-sets that will surely have p-values below the specified significance level. This reduces dramatically the computational complexity of the approach while fully respecting the standard CP guarantees. Our experimental results show that the contextualised-based classifier surpasses the non-contextualised-based ones and obtains state-of-the-art performance for all data-sets examined. The good performance of the underlying classifiers is carried on to their ICP counterparts without any significant accuracy loss, but with the added benefits of ICP, i.e. the confidence information encapsulated in the prediction sets. We experimentally demonstrate that the resulting prediction sets can be tight enough to be practically useful even though the set of all possible label-sets contains more than 1e+16 combinations. Additionally, the empirical error rates of the obtained prediction-sets confirm that our outputs are well-calibrated.

Application of multi-label classification models for the diagnosis of diabetic complications

BackgroundEarly diagnosis for the diabetes complications is clinically demanding with great significancy. Regarding the complexity of diabetes complications, we applied a multi-label classification (MLC) model to predict four diabetic complications simultaneously using data in the modern electronic health records (EHRs), and leveraged the correlations between the complications to further improve the prediction accuracy.MethodsWe obtained the demographic characteristics and laboratory data from the EHRs for patients admitted to Changzhou No. 2 People’s Hospital, the affiliated hospital of Nanjing Medical University in China from May 2013 to June 2020. The data included 93 biochemical indicators and 9,765 patients. We used the Pearson correlation coefficient (PCC) to analyze the correlations between different diabetic complications from a statistical perspective. We used an MLC model, based on the Random Forest (RF) technique, to leverage these correlations and predict four complications simultaneously. We explored four different MLC models; a Label Power Set (LP), Classifier Chains (CC), Ensemble Classifier Chains (ECC), and Calibrated Label Ranking (CLR). We used traditional Binary Relevance (BR) as a comparison. We used 11 different performance metrics and the area under the receiver operating characteristic curve (AUROC) to evaluate these models. We analyzed the weights of the learned model and illustrated (1) the top 10 key indicators of different complications and (2) the correlations between different diabetic complications.ResultsThe MLC models including CC, ECC and CLR outperformed the traditional BR method in most performance metrics; the ECC models performed the best in Hamming loss (0.1760), Accuracy (0.7020), F1_Score (0.7855), Precision (0.8649), F1_micro (0.8078), F1_macro (0.7773), Recall_micro (0.8631), Recall_macro (0.8009), and AUROC (0.8231). The two diabetic complication correlation matrices drawn from the PCC analysis and the MLC models were consistent with each other and indicated that the complications correlated to different extents. The top 10 key indicators given by the model are valuable in medical application.ConclusionsOur MLC model can effectively utilize the potential correlation between different diabetic complications to further improve the prediction accuracy. This model should be explored further in other complex diseases with multiple complications.

WITHDRAWN: Equivalence classifier chain Label power set tokenization of toxic comment multi label classification using machine learning

Appropriate discipline and decorum maintenance is the essential requirement on the online platform with the growth of technology. Many uncivilized threat and degrading messages are sent by the unauthorized users in the electronic mails and mobile messages. The process of exact identification and removal of toxic messages from the social media is critical and still remains a challenging issue. With this background, this paper provides the following contribution towards predicting the toxic comment classification. Firstly, the Jigsaw toxic comment classification dataset extracted from KAGGLE repository is subjected with the data pre-processing. Secondly, the distribution of the toxic behaviour classes is identified and depicted as pictorial representations. Thirdly, TFIDF is used for segregating the most likely features of the dataset with the methodology of both unigrams and bigrams. Fourth, the removed Counter vectorized features from the toxic comment classification dataset is fitted to various classifiers and the efficiency is analysed for binary equivalence, Classifier chain and Label power setclassification. Fifth, the extracted TFID vectorized features from the toxic comment classification dataset is fitted to various classifiers and the efficiency is analysed for binary equivalence, Classifier chain and Label power setclassification. Sixth, the extracted Hash vectorized features from the toxic comment classification dataset is fitted to various classifiers and the efficiency is analysed for binary equivalence, Classifier chain and Label power setclassification. The efficiency analysis is done with the use of evaluation metrics of the classification. Experimental results shows that Kernel SVM classifier have the 87% accuracy with the binary equivalence classification for all the tokenization methods. Random Forest classifier have the accuracy of 88% with the classifier chain and 87% with label power set classification for all the tokenization methods.

Addressing the multi-label imbalance for neural networks: An approach based on stratified mini-batches

Imbalanced learning in the multi-label scenario is a challenging issue, and it also exists in the training of deep neural network. Previous studies have demonstrated that the resampling methods are capable of reducing bias towards the majority group. Nonetheless, when being extended to neural network, these methods display some obvious drawbacks, such as the introduction of extra hyperparameters, the fixed training mode, etc. In order to eliminate the disadvantages, in this paper, an efficient training technique named Mini-Batch Gradient Descent with Stratified sampling (MBGD-Ss) is proposed to alliviate the issue with imbalanced data by dynamically sampling. In view of the specialty of multi-label domain, we put forward two specific strategies as Label Powerset based (SsLP) and Label-based (SsL), respectively. Particularly, SsLP takes the label combination (labelset) that appears in the dataset as a stratum, and SsL directly sets the label as a stratum. Extensive experiments validate the effectiveness of the proposed approach in decreasing the imbalance of sampled data. Moreover, the empirical analysis also shows that the proposed method can mitigate the classifier’s bias against labels, especially improve the prediction accuracy of minority labels.

Multilabel Video Classification Model of Navigation Mark's Lights Based on Deep Learning.

At night, buoys and other navigation marks disappear to be replaced by fixed or flashing lights. Navigation marks are seen as a set of lights in various colors rather than their familiar outline. Deciphering that the meaning of the lights is a burden to navigators, it is also a new challenging research direction of intelligent sensing of navigation environment. The study studied initiatively the intelligent recognition of lights on navigation marks at night based on multilabel video classification methods. To capture effectively the characteristics of navigation mark's lights, including both color and flashing phase, three different multilabel classification models based on binary relevance, label power set, and adapted algorithm were investigated and compared. According to the experiment's results performed on a data set with 8000 minutes video, the model based on binary relevance, named NMLNet, has highest accuracy about 99.23% to classify 9 types of navigation mark's lights. It also has the fastest computation speed with least network parameters. In the NMLNet, there are two branches for the classifications of color and flashing, respectively, and for the flashing classification, an improved MobileNet-v2 was used to capture the brightness characteristic of lights in each video frame, and an LSTM is used to capture the temporal dynamics of lights. Aiming to run on mobile devices on vessel, the MobileNet-v2 was used as backbone, and with the improvement of spatial attention mechanism, it achieved the accuracy near Resnet-50 while keeping its high speed.

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.

Active k-labelsets ensemble for multi-label classification

The random k-labelsets ensemble (RAkEL) is a multi-label learning strategy that integrates many single-label learning models. Each single-label model is constructed using a label powerset (LP) technique based on a randomly generated size-k label subset. Although RAkEL can improve the generalization capability and reduce the complexity of the original LP method, the quality of the randomly generated label subsets could be low. On the one hand, the transformed classes may be difficult to separate in the feature space, negatively affecting the performance; on the other hand, the classes might be highly imbalanced, resulting in difficulties in using the existing single-label algorithms. To solve these problems, we propose an active k-labelsets ensemble (ACkEL) paradigm. Borrowing the idea of active learning, a label-selection criterion is proposed to evaluate the separability and balance level of the classes transformed from a label subset. Subsequently, by randomly selecting the first label or label subset, the remaining ones are iteratively chosen based on the proposed criterion. ACkEL can be realized in both the disjoint and overlapping modes, which adopt pool-based and stream-based frameworks, respectively. Experimental comparisons demonstrate the feasibility and effectiveness of the proposed methods.

Evolving multi-label classification rules by exploiting high-order label correlations

In multi-label classification tasks, each problem instance is associated with multiple classes simultaneously. In such settings, the correlation between labels contain valuable information that can be used to obtain more accurate classification models. The correlation between labels can be exploited in different levels such as capturing the pair-wise correlation or exploiting the higher-order correlations. Even though the high-order approach is more capable of modeling the correlation, it is computationally more demanding and has scalability issues. This paper aims at exploiting the high-order label correlations locally using supervised learning classifier systems (UCS). For this purpose, the label powerset (LP) strategy is employed and a prediction aggregation is utilized that improves the prediction capability of the LP method in the presence of unseen labelsets. Exact match ratio and Hamming loss measures are considered to evaluate the rule performance and the expected fitness value of individual classification rules is investigated using both metrics. Also, a computational complexity analysis is provided for training the proposed algorithm. The experimental results of the proposed method are compared with other well-known LP-based methods on multiple benchmark datasets and confirm the competitive performance of this method.

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.

Drug Target Group Prediction with Multiple Drug Networks.

Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

Aspect-Based Sentiment Analysis and Emotion Detection for Code-Mixed Review

Review can affect customer decision making because by reading it, people manage to know whether the review is positive, or negative. However, positive, negative, and neutral, without considering the emotion will be not enough because emotion can strengthen the sentiment result. This study explains about the comparison of machine learning and deep learning in sentiment as well as emotion classification with multi-label classification. In machine learning comparison, the problem transformation that we used are Binary Relevance (BR), Classifier Chain (CC), and Label Powerset (LP), with Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extra Tree Classifier (ET) as algorithms of machine learning. The features we compared are n-gram language model (unigram, bigram, unigram-bigram). For deep learning, algorithms that we applied are Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM), using self-developed word embedding. The comparison results show RF dominates with 88.4% and 89.54% F1 scores with CC method for food aspect, and LP for price, respectively. For service and ambience aspects, ET leads with 92.65% and 87.1% with LP and CC methods, respectively. On the other hand, in deep learning comparison, GRU and BiLSTM obtained similar F1- score for food aspect, 88.16%. On price aspect, GRU leads with 83.01%. However, for service and ambience, BiLSTM achieved higher F1-score, 89.03% and 84.78%.

A Machine Learning-Based Recommender System for Improving Students Learning Experiences

Outcome-based education (OBE) is a well-proven teaching strategy based upon a predefined set of expected outcomes. The components of OBE are Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs). These latter are assessed at the end of each course and several recommended actions can be proposed by faculty members' to enhance the quality of courses and therefore the overall educational program. Considering a large number of courses and the faculty members' devotion, bad actions could be recommended and therefore undesirable and inappropriate decisions may occur. In this paper, a recommender system, using different machine learning algorithms, is proposed for predicting suitable actions based on course specifications, academic records, and course learning outcomes' assessments. We formulated the problem as a multi-label multi-class binary classification problem and the dataset was translated into different problem transformation and adaptive methods such as one-vs.-all, binary relevance, label powerset, classifier chain, and ML-KNN adaptive classifier. As a case study, the proposed recommender system is applied to the college of Computer and Information Sciences, Jouf University, Kingdom of Saudi Arabia (KSA) for helping academic staff improving the quality of teaching strategies. The obtained results showed that the proposed recommender system presents more recommended actions for improving students' learning experiences.

A multi-label learning model for psychotic diseases in Nigeria

Abstract The goal of Multi-Label Classification (MLC) is to allot an instance to a set of different labels. This task is usually addressed by either transforming the problem into several binary problems, adapting machine learning models to fit multi-label data or create an ensemble of models that can classify multi-label datasets. The communal relationship between Bipolar, Insomnia, Schizophrenia, Vascular Dementia (VD) and Attention-Deficit/Hyperactivity Disorder (ADHD) in the Psychotic Disorder Diseases (PDD) motivate the research for a diagnostic method that classifies and evaluates each psychotic disorder simultaneously. This study experimentally evaluates 15 MLC methods using 10 evaluation measures over a new PDD dataset. The performance of these methods is measured with four ranking - based, three example-based and three label-based measures. Also, the efficiency of these methods is measured by their 90-10 Train-Test split with the 10 evaluation measures. The results show that the Label Powerset (LC) and Pruned Sets (PS), MLC methods with Naive Bayes (NB) and Naive Bayes Tree (NBTree) consistently performed best in terms of the evaluation measures on the PDD dataset. Schizophrenia has the highest classification accuracy with Bipolar the lowest in the data split of 90-10. Logistic model tree (LMT) is the best algorithm for Insomnia and Bipolar while Naive Bayes (NB) is the best for Schizophrenia, VD and MBD. Support vector machines (SVM) with ensemble learning and classification (ELC) and Ensemble of Pruned Set (EPS) are the best classifiers for Bipolar while SVM with regression and threshold (RT) is the least. The classifiers are statistically significantly different for Insomnia, VD and ADHD only.

A Novel Deep Neural Network Model for Multi-Label Chronic Disease Prediction.

Chronic diseases are one of the biggest threats to human life. It is clinically significant to predict the chronic disease prior to diagnosis time and take effective therapy as early as possible. In this work, we use problem transform methods to convert the chronic diseases prediction into a multi-label classification problem and propose a novel convolutional neural network (CNN) architecture named GroupNet to solve the multi-label chronic disease classification problem. Binary Relevance (BR) and Label Powerset (LP) methods are adopted to transform multiple chronic disease labels. We present the correlated loss as the loss function used in the GroupNet, which integrates the correlation coefficient between different diseases. The experiments are conducted on the physical examination datasets collected from a local medical center. In the experiments, we compare GroupNet with other methods and models. GroupNet outperforms others and achieves the best accuracy of 81.13%.

Multi-label biomedical question classification for lexical answer type prediction.

Question classification is considered one of the most significant phases of a typical Question Answering (QA) system. It assigns certain answer types to each question which leads to narrow down the search space of possible answers for factoid and list type questions. The process of assigning certain answer types to each question is also known as Lexical Answer Type (LAT) Prediction. Although much work has been done to enhance the performance of question classification into coarse and fine classes in diverse domains, it is still considered a challenging task in the biomedical field. The difficulty in biomedical question classification stems from the fact that one question might have more than one label or expected answer types associated with it (also, referred to as a multi-label classification). In the biomedical domain, only preliminary work is done to classify multi-label questions by transforming them into a single label through copy transformation technique. In this paper, we have generated a multi-labeled corpus (MLBioMedLAT) by exploring the process of Open Advancement of Question Answering (OAQA) system for the task of biomedical question classification. We use 780 biomedical questions from BioASQ challenge and assign them appropriate labels. To annotate these labels, we use the answers for each question and assign the question semantic type labels by leveraging an existing corpus and utilizing OAQA system. The paper introduces a data transformation approach namely Label Power Set with logistic regression (LPLR) for the task of multi-label biomedical question classification and compares its performance with Structured SVM (SSVM), Restricted Boltzmann Machine (RBM), and copy transformation based logistic regression (CLR) (previously used for a similar task in the OAQA system). To evaluate the integrity of the introduced data transformation technique, we use three prominent evaluation measures namely MicroF1, Accuracy, and Hamming Loss. Regarding MicroF1, our introduced technique coupled with a new feature set surpasses CLR, SSVM, and RBM with a margin of 7%, 8%, and 22% respectively.

Co-learning binary classifiers for LP-based multi-label classification

A simple yet practical multi-label learning method, called label powerset (LP), considers each different combination of labels that appear in the training set as a different class value of a single-label classification task. However, because those classes source from multiple labels, there may be some inherent relationships among them. To tackle this challenge, we propose a novel model which aims to co-learn binary classifiers, by combining the training of binary classifiers and the characterizing the relationship among them into a unified objective function. In addition, we develop an alternating optimization algorithm to solve the proposed problem. Extensive experimental results on various kinds of datasets well demonstrate the effectiveness of the proposed model.

Sensor analytics for interpretation of EKG signals

Motivation and ObjectivesSmartphones are emerging as personal fitness assistants, collecting data through in-built or external sensors. The next frontier for these devices is to use advanced sensors and machine learning algorithms to offer more personalized and advanced medical assessments. Along these lines, the objective of this paper is to develop a multi-label classification model to detect heart complications through electrocardiograms (EKGs) collected by an FDA-approved single-lead EKG sensor attached to a smartphone. The EKG sensor produces a standard EKG chart, but for such a sensor to be useful to a consumer, an interpretation of the graph is necessary. Materials and MethodWe adapt a machine-learning approach to detect multiple heart conditions simultaneously from the generated EKG graph. Three different multi-label machine learning models (binary relevance, label powerset and multi-perceptron neural network) were built and compared to categorize five different heart states: Normal, Atrial Fibrillation, Atrioventricular Block, Sinus Bradycardia and Sinus Tachycardia. The binary relevance model was selected based on the accuracy. Results and ImplicationsThe model generated rules inductively from the data to interpret nine out of every ten heart conditions correctly. Our model is being adapted for commercial use by a company (as a part of their App) that markets the EKG sensor for smartphones. Our model is usable in a cardiovascular disease alert expert system that will potentially allow users to monitor their heart health continuously and prevent a serious illness by providing this information in the early stages.

Multi-label Arabic text categorization: A benchmark and baseline comparison of multi-label learning algorithms

Multi-label text categorization refers to the problem of assigning each document to a subset of categories by means of multi-label learning algorithms. Unlike English and most other languages, the unavailability of Arabic benchmark datasets prevents evaluating multi-label learning algorithms for Arabic text categorization. As a result, only a few recent studies have dealt with multi-label Arabic text categorization on non-benchmark and inaccessible datasets. Therefore, this work aims to promote multi-label Arabic text categorization through (a) introducing “RTAnews”, a new benchmark dataset of multi-label Arabic news articles for text categorization and other supervised learning tasks. The benchmark is publicly available in several formats compatible with the existing multi-label learning tools, such as MEKA and Mulan. (b) Conducting an extensive comparison of most of the well-known multi-label learning algorithms for Arabic text categorization in order to have baseline results and show the effectiveness of these algorithms for Arabic text categorization on RTAnews. The evaluation involves four multi-label transformation-based algorithms: Binary Relevance, Classifier Chains, Calibrated Ranking by Pairwise Comparison and Label Powerset, with three base learners (Support Vector Machine, k-Nearest-Neighbors and Random Forest); and four adaptation-based algorithms (Multi-label kNN, Instance-Based Learning by Logistic Regression Multi-label, Binary Relevance kNN and RFBoost). The reported baseline results show that both RFBoost and Label Powerset with Support Vector Machine as base learner outperformed other compared algorithms. Results also demonstrated that adaptation-based algorithms are faster than transformation-based algorithms.