Filters








194,677 Hits in 3.1 sec

Ensemble Learning for Relational Data

Hoda Eldardiry, Jennifer Neville, Ryan A. Rossi
2020 Journal of machine learning research  
In addition, we propose a relational ensemble framework that combines a relational ensemble learning approach with a relational ensemble inference approach for collective classification.  ...  We show that ensembles of collective classifiers can improve predictions for graph data by reducing errors due to variance in both learning and inference.  ...  Acknowledgments We thank Luc De Raedt for many insightful suggestions and feedback that greatly improved the manuscript. We also thank all the reviewers for many helpful suggestions and feedback.  ... 
dblp:journals/jmlr/EldardiryNR20 fatcat:ai42eks7zbf7fi3tco4k3cjw54

An analysis of how ensembles of collective classifiers improve predictions in graphs

Hoda Eldardiry, Jennifer Neville
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
We present an empirical framework that includes various ensemble techniques for classifying relational data using collective inference.  ...  We present a theoretical analysis framework that shows how ensembles of collective classifiers can improve predictions for graph data.  ...  Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation hereon.  ... 
doi:10.1145/2396761.2396793 dblp:conf/cikm/EldardiryN12 fatcat:7ac3s5usuzf7lfyka45antz2w4

Time-Evolving Relational Classification and Ensemble Methods [chapter]

Ryan Rossi, Jennifer Neville
2012 Lecture Notes in Computer Science  
We propose a novel framework for discovering temporal-relational representations for classification.  ...  However, accurately incorporating the full range of temporal dependencies into relational learning algorithms remains a challenge.  ...  learning an ensemble of models from several versions of the data.  ... 
doi:10.1007/978-3-642-30217-6_1 fatcat:ngbev72235grrkofuhb42vzoiu

An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere [article]

Han Hu and NhatHai Phan and James Geller and Stephen Iezzi and Huy Vo and Dejing Dou and Soon Ae Chun
2019 arXiv   pre-print
., many studies that primarily utilize social media data, such as postings on Twitter, to study drug abuse-related activities use machine learning as a powerful tool for text classification and filtering  ...  This imbalanced data remains a major issue in building effective tweet classifiers, and is especially obvious for studies that include abuse-related slang terms.  ...  For the ensemble deep learning model, six models of three types (two for each type) are used.  ... 
arXiv:1904.02062v1 fatcat:vdmfs4rctnde5dbixgbna2i3xy

An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere

Han Hu, NhatHai Phan, James Geller, Stephen Iezzi, Huy Vo, Dejing Dou, Soon Ae Chun
2019 Studies in Health Technology and Informatics  
., many studies that primarily utilize social media data, such as postings on Twitter, to study drug abuse-related activities use machine learning as a powerful tool for text classification and filtering  ...  This imbalanced data remains a major issue in building effective tweet classifiers, and is especially obvious for studies that include abuse-related slang terms.  ...  For the ensemble deep learning model, six models of three types (two for each type) are used.  ... 
doi:10.3233/shti190204 pmid:31437906 fatcat:yxx7ne7xb5ckdhwqm4zx75avxi

Augmented Machine Learning Ensemble Extension Model for Social Media Health Trends Predictions

2019 International journal of recent technology and engineering  
Using this model, we will see how Ensemble Machine Learning based Analytical Model for analyzing social network data for health topics is efficient than traditional Machine Learning technique(s).  ...  In the past several models have been propounded for various machine learning based analytics for the Social Networks study but there is a perceived need for studying social networks for health data using  ...  Need for Ensemble Machine Learning Some of the reasons for using Ensemble Machine Learning are related to dataset characteristics and training confidence. Some of the reasons are as follows.  ... 
doi:10.35940/ijrte.b1091.0782s719 fatcat:f57k7bqqojg4vimz7vqrvjmxnu

Transferred correlation learning: An incremental scheme for neural network ensembles

Lei Jiang, Jian Zhang, Gabrielle Allen
2010 The 2010 International Joint Conference on Neural Networks (IJCNN)  
For example, out-dated data can be used as such related data. In this paper, we propose a new transfer learning framework for training neural network (NN) ensembles.  ...  Transfer learning is a new learning paradigm, in which, besides the training data for the targeted learning task, data that are related to the task (often under a different distribution) are also employed  ...  Instead, we often encounter situations in which we have plenty of data from related learning tasks and the training data for the targeted task are scarce (or expensive to obtain).  ... 
doi:10.1109/ijcnn.2010.5596617 dblp:conf/ijcnn/JiangZA10 fatcat:kj34i5rzmfdihgjbzipxsu3tlm

Hybrid Model using Stack-Based Ensemble Classifier and Dictionary Classifier to Improve Classification Accuracy of Twitter Sentiment Analysis

Sangeeta Rani
2020 International Journal of Emerging Trends in Engineering Research  
Set, 0.8881453 for data set related to 'Clean India Mission' and 0.9953593 for Sentiment 140 Twitter Data Set, as compared to machine learning classifiers and other ensemble classifiers.  ...  Kaggle -US Airline Twitter Sentiment Data Set, Sentiment 140 Twitter Data Set, and Real time manually labeled data set related to 'Clean India Mission' are used for the implementation of the proposed model  ...  Time Twitter Data Set Related to 'Clean India Mission' and 0.99559 for Sentiment 140 Twitter Data sub set.  ... 
doi:10.30534/ijeter/2020/02872020 fatcat:z6k4ia4bdre4pj7g6j4herm4rm

A Review on Semi-Supervised Relation Extraction [article]

Yusen Lin
2021 arXiv   pre-print
To reduce the expensive annotation efforts, semisupervised learning aims to leverage both labeled and unlabeled data.  ...  In this paper, we review and compare three typical methods in semi-supervised RE with deep learning or meta-learning: self-ensembling, which forces consistent under perturbations but may confront insufficient  ...  ., 2019) are chosen as the examples for self-ensembling and dual learning, respectively.  ... 
arXiv:2103.07575v1 fatcat:bgbmraci4je3bptazpng6dgecm

The impact of parameter optimization of ensemble learning on defect prediction

Muhammed Maruf Ozturk
2019 Computer Science Journal of Moldova  
Further, this paper presents a new ensemble learning algorithm called novelEnsemble for defect prediction data sets. The method has been tested on 27 data sets.  ...  set; 5) Each ensemble learning approach may not create a favorable effect on HO.  ...  Related Works Related works can be examined in twofold: these are ensemble learning and HO studies which are closely related to defect prediction.  ... 
doaj:2434dbe4a2c4453aaaaee90edc0fd22e fatcat:mcmxsxes7berfexgw7u7z75sb4

Conceptual framework for neuronal ensemble identification and manipulation related to behavior using calcium imaging

Luis Carrillo-Reid, Vladimir Calderon
2022 Neurophotonics  
Conclusions: The use of simultaneous two-photon calcium imaging and two-photon optogenetics allowed for the experimental demonstration of the causal relation of population activity and learned behaviors  ...  learned behaviors.  ...  experiments is the fact that the reactivation of targeted ensembles related to a learned behavior can evoke such behavior and that the targeting of different ensembles cannot evoke the learned behavior  ... 
doi:10.1117/1.nph.9.4.041403 pmid:35898958 pmcid:PMC9309498 fatcat:ruc7mq2h4jhhbmgjkdx5aubycq

An Ensemble-Based Decision Tree Approach for Educational Data Mining

Moloud Abdar, Mariam Zomorodi-Moghadam, Xujuan Zhou
2018 2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC)  
The aim of this study is to use different data mining and machine learning algorithms on actual data sets related to students. To this end, we apply two decision tree methods.  ...  Nowadays, data mining and machine learning techniques are applied to a variety of different topics (e. g., healthcare and disease, security, decision support, sentiment analysis, education, etc.).  ...  For this purpose, RandomTree and REPTree algorithms were used as base methods for Rotation Forest. Therefore, the ensemble methods were applied on the data set.  ... 
doi:10.1109/besc.2018.8697318 dblp:conf/besc/AbdarMZ18 fatcat:o65h62vdy5fdllyxoqwcvchtsq

Negative correlation in incremental learning

Fernanda Li Minku, Hirotaka Inoue, Xin Yao
2007 Natural Computing  
The difference among the neural networks that compose an ensemble is a desirable feature to perform incremental learning, for some of the neural networks can be able to adapt faster and better to new data  ...  In this way, it is important to find a trade-off between overcoming catastrophic forgetting and using an entire ensemble to learn new data.  ...  The authors are grateful to the guest editor, Professor Bogdan Gabrys, and anonymous referees for their valuable comments, which have helped to improve the quality of this paper.  ... 
doi:10.1007/s11047-007-9063-7 fatcat:rgooeh6b75dctbip44alt6g654

The Development and Testing of Interactive Multimedia Learning Materials for Teaching and Learning of Tumbuk Kalang Music Ensemble in Formal Educational Institutions

Mohd.NizamNasrifan Et.al
2021 Turkish Journal of Computer and Mathematics Education  
and learning materials for Tumbuk Kalang Music ensemble of Negeri Sembilan in formal educational institutions.  ...  An interactive multimedia and learning material for Tumbuk Kalang Music ensemble has been developed and evaluated for its suitability to be used in formal educational institutions.  ...  Section B: Instructional Design A questionnaire related to the development of interactive instructional design for teaching and learning of TumbukKalang music ensemble is divided into five 5 sections including  ... 
doi:10.17762/turcomat.v12i3.831 fatcat:lhckmbz5fvgvxnlhw3vczgwzpm

Relational Ensemble Classification

Christine Preisach, Lars Schmidt-Thieme
2006 IEEE International Conference on Data Mining. Proceedings  
Furthermore, we discuss solutions for several problems concerning relational data such as heterogeneity, sparsity, and multiple relations.  ...  In this paper we introduce a new approach to make use of several relations as well as both relations and attributes for classification using ensemble methods.  ...  Furthermore we thank Robert Koppa for the implementation of the text classifier we used.  ... 
doi:10.1109/icdm.2006.135 dblp:conf/icdm/PreisachS06 fatcat:iy2mfdcg3zhqxayyxjmvofbje4
« Previous Showing results 1 — 15 out of 194,677 results