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Student Performance Prediction with Optimum Multilabel Ensemble Model
2021
Journal of Intelligent Systems
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to transform each labelset into a multi-class classification task. ...
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 ...
Acknowledgement: The authors would like to acknowledge Ethiopian Institute of Technology -Mekelle for supporting this work during the collection of dataset by writing a letters of request to high school ...
doi:10.1515/jisys-2021-0016
fatcat:5ikjkpfztfednk3qvexmws2weq
k-Labelsets Method for Multi-Label ECG Signal Classification Based on SE-ResNet
2021
Applied Sciences
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In addition, the random k-labelsets (RAKEL) algorithm was applied to improve the performance in multi-label classification problems. ...
In this study, we propose a method for performing multi-label classification on standard ECG (12-lead with duration of 10 s) data. ...
Multi-Label Classification Results Based on Subclass Table 4 shows the multi-label classification results of the proposed model with k-labelsets based on the subclass. ...
doi:10.3390/app11167758
fatcat:pb5tcknkfjeqvn6zvgnzprbgbu
Auto-adaptive Grammar-Guided Genetic Programming algorithm to build Ensembles of Multi-Label Classifiers
2021
Information Fusion
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The first, fixed, uses the same value of 𝑘 for all multi-label classifiers in the ensemble. ...
It creates a tree-shaped ensemble, where each leaf is a multi-label classifier focused on a subset of 𝑘 labels. ...
Tables A.1, A.2, A.3, A.4, A.5 and A.6 include the results for AHL, SA, ExF, MiF, MaF, and runtime, respectively. ...
doi:10.1016/j.inffus.2021.07.005
fatcat:ebd5qhgannfilbn6pgoaffd6my
Multi-label ECG Signal Classification Based on Ensemble Classifier
2020
IEEE Access
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It provides a feasible analysis method for multi-label ECG signal automatic classification. INDEX TERMS Electrocardiogram, multi-label classification, ensemble classification, mutual information. ...
To resolve the multi-label ECG signal classification problems, we propose a novel ensemble multi-label classification model in this paper. ...
A random k-labelsets for multilabel classification is proposed in reference [39] . The labelset is sampled into small scale labelsets randomly. ...
doi:10.1109/access.2020.3004908
fatcat:bjl5ugj7gbc3lhvs7tr5umlrn4
Student Performance Prediction with Optimum Multilabel Ensemble Model
[article]
2019
arXiv
pre-print
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transform each labelset into a multi-class classification task. ...
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 Mult-layer perceptron (MLP) as base-classifiers ...
Multi-label Ensemble Model Our dataset contains five output labels which makes it a multi-label classification task. ...
arXiv:1909.07444v1
fatcat:zkydzkohnvbgllzm6stc6ek6qe
Multi-label kNN Classifier with Self Adjusting Memory for Drifting Data Streams
2018
European Conference on Principles of Data Mining and Knowledge Discovery
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This paper presents a multi-label k Nearest Neighbor (kNN) with Self Adjusting Memory (SAM) for drifting data streams (ML-SAM-kNN). ...
The experimental study compares the proposal with eight other multi-label classifiers for data streams on 23 datasets on six multi-label metrics, evaluation time, and memory consumption. ...
k Nearest Neighbor k: 10 window: 1000 ML-SAM-kNN Multi-label Self Adjusting Memory k: 5 k Nearest Neighbor maxSTM: 400 maxLTM: 600
Table 3 : 3 Results for subset accuracy. ...
dblp:conf/pkdd/RoseberryC18
fatcat:j6vu2akys5hs5dynmigktiya3q
Cost-Sensitive Multi-Label Learning for Audio Tag Annotation and Retrieval
2011
IEEE transactions on multimedia
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By considering the co-occurrences of tags, we can model the audio tagging problem as a multi-label classification problem. ...
This can be achieved by training a binary classifier for each tag based on the labeled music data. Our method that won the MIREX 2009 audio tagging competition is one of this kind of methods. ...
Our work is based on two multi-label classification algorithms: stacking [17] and random k-Labelsets (RAkEL) [18] . We extend these two methods for cost-sensitive multi-label classification.
B. ...
doi:10.1109/tmm.2011.2129498
fatcat:oqhkkd755rbuhhe4zoq6b6n4cy
Local Multi-Label Explanations for Random Forest
[article]
2022
arXiv
pre-print
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Multi-label classification is a challenging task, particularly in domains where the number of labels to be predicted is large. ...
Deep neural networks are often effective at multi-label classification of images and textual data. ...
Acknowledgments The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the "First Call for H.F.R.I. ...
arXiv:2207.01994v1
fatcat:v6vvokgwbrfbjp2xobse3uohse
Multi-label classification search space in the MEKA software
[article]
2020
arXiv
pre-print
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This supplementary material aims to describe the proposed multi-label classification (MLC) search spaces based on the MEKA and WEKA softwares. ...
Second, we review 28 single-label classification (SLC) algorithms, preprocessing algorithms and meta-algorithms in the WEKA software. ...
Ensemble of Multi-Label Classifiers The ensemble of multi-label classifiers (EnsembleML) [47] is an algorithm that combines several multi-label classifiers in a simplesubset ensemble. ...
arXiv:1811.11353v4
fatcat:s4aeh52ztnbcbbprii62byatge
In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data
2017
Wiley Interdisciplinary Reviews. Computational Molecular Science
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This can be achieved through multi-label classification methods. These methods have not undergone comprehensive benchmarking in the domain of predictive toxicology. ...
Therefore, we performed extensive benchmarking and analysis of over 19,000 multi-label classification models generated using combinations of the state-of-the-art methods. ...
for multi-label classification. ...
doi:10.1002/wcms.1352
pmid:29780432
pmcid:PMC5947741
fatcat:y3wy2umckjbapnzgephde2k3l4
Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2105.12364v1 fatcat:nxy7u5xuy5bwzffuxz7bw5piam
In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers. ...
This suggests that these algorithms are superior in addressing the widely known data imbalance problem in multi-label learning. ...
For multi-label classification, we implement our own one-vs-all model using a Python library named sci-kit learn ...
arXiv:2105.12364v2
fatcat:6ru2nlov7nhhbf7b2x66lnz7o4
Evolving Multi-label Classification Rules by Exploiting High-order Label Correlation
[article]
2020
arXiv
pre-print
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In multi-label classification tasks, each problem instance is associated with multiple classes simultaneously. ...
of the LP method in the presence of unseen labelsets. ...
Random -labelset (RA EL) [41] exploits label correlation in a random way by transforming the problem into an ensemble of multi-class classifica-tion problems where each component of the ensemble learns ...
arXiv:2007.11609v1
fatcat:l3elyf4omfblrgq2odbhsaccs4
A Review on Multi-Label Learning Algorithms
2014
IEEE Transactions on Knowledge and Data Engineering
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As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes. ...
Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. ...
For an ensemble created by n k-labelsets, the maximum number of votes on each label is nk/q on average. A rule-of-thumb setting for Random k-Labelsets is k = 3 and n = 2q [92] , [94] . ...
doi:10.1109/tkde.2013.39
fatcat:oqvq3cei4vatdld4j4bqeyc7ry
Multilabel Classification Methods for Human Activity Recognition: A Comparison of Algorithms
2022
Sensors
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Activity recognition for multiple residents acting concurrently can be treated as a multilabel classification problem (MLC). ...
While this is an important area, especially when focusing on elderly people living alone, multi-resident activity recognition has potentially more applications in smart homes. ...
Acknowledgments: We would like to thank the reviewers for taking the time and effort necessary to review the manuscript. ...
doi:10.3390/s22062353
pmid:35336522
pmcid:PMC8955852
fatcat:tb7e7b473vdkzkvvkgvatex7ty
WISE 2014 Challenge: Multi-label Classification of Print Media Articles to Topics
[chapter]
2014
Lecture Notes in Computer Science
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The WISE 2014 challenge was concerned with the task of multi-label classification of articles coming from Greek print media. ...
Building multi-label classifiers for the automated annotation of articles into topics can support the work of human annotators by suggesting a list of all topics by order of relevance, or even automate ...
The 2nd team created a much larger ensemble, of over 200 classifiers, by employing a variety of multi-label classification algorithms (binary relevance, classifier chains, (pruned) label powerset, random ...
doi:10.1007/978-3-319-11746-1_40
fatcat:blghkjhktbc5hclq7qef4ljcp4
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