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News Articles Classification Using Random Forests and Weighted Multimodal Features [chapter]

Dimitris Liparas, Yaakov HaCohen-Kerner, Anastasia Moumtzidou, Stefanos Vrochidis, Ioannis Kompatsiaris
<span title="">2014</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
This research investigates the problem of news articles classification.  ...  the software tool to generate the textual features used in this research.  ...  on Integrating Vision and Language (iV&L Net).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-12979-2_6">doi:10.1007/978-3-319-12979-2_6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wlvrswhuvzfhhjdtb6a3q3lipq">fatcat:wlvrswhuvzfhhjdtb6a3q3lipq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170202230644/http://mklab.iti.gr/files/IRFC%202014-Draft.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/05/a5/05a54d905ee5852c96b517c73df0783895e17669.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-12979-2_6"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

Mohammadreza Soltaninejad, Guang Yang, Tryphon Lambrou, Nigel Allinson, Timothy L Jones, Thomas R Barrick, Franklyn A Howe, Xujiong Ye
<span title="">2018</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yejgm3i4j5ehxbahbzb42nncke" style="color: black;">Computer Methods and Programs in Biomedicine</a> </i> &nbsp;
Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue.  ...  Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate  ...  [15] proposed a new random forest based method which uses domain adaptation to reduce sample selection errors. Bauer et al. [16] proposed using RF and conditional random fields (CRF).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.cmpb.2018.01.003">doi:10.1016/j.cmpb.2018.01.003</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29477436">pmid:29477436</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5tcuxabopfh5zk2fe4rcpqme7y">fatcat:5tcuxabopfh5zk2fe4rcpqme7y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190430080546/http://eprints.lincoln.ac.uk/31055/1/Multimodal%20MRI%20Brain-final.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b8/82/b882a62eed4f3af4f0a0496d7f4626fab32f6694.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.cmpb.2018.01.003"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

MLW-gcForest: a multi-weighted gcForest model towards the staging of lung adenocarcinoma based on multi-modal genetic data

Yunyun Dong, Wenkai Yang, Jiawen Wang, Juanjuan Zhao, Yan Qiang, Zijuan Zhao, Ntikurako Guy Fernand Kazihise, Yanfen Cui, Xiaotong Yang, Siyuan Liu
<span title="2019-11-14">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
First, different weights are assigned to different random forests according to the classification performance of these forests in the standard gcForest model.  ...  Second, because the feature vectors generated under different scanning granularities have a diverse influence on the final classification result, the feature vectors are given weights according to the  ...  Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-019-3172-z">doi:10.1186/s12859-019-3172-z</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31726986">pmid:31726986</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6857238/">pmcid:PMC6857238</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6mmilextwvesbdevwkwkgodyyu">fatcat:6mmilextwvesbdevwkwkgodyyu</a> </span>
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Desk Organization: Effect of Multimodal Inputs on Spatial Relational Learning

Ryan Rowe, Shivam Singhal, Daqing Yi, Tapomayukh Bhattacharjee, Siddhartha S. Srinivasa
<span title="">2019</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/f576vta6inh3nchhvhkef7gxjq" style="color: black;">2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)</a> </i> &nbsp;
We use two types of models: random forests, which focus on precise multi-task classification, and Markov logic networks, which provide an easily interpretable insight into organizational habits.  ...  The models were applied to both synthetic data, which proved to be learnable when using fixed organizational constraints, and human-study data, on which the random forest achieved over 90 V}modalities,  ...  Some examples include classification of Alzheimer's disease [29] , automatic job-candidate screening based on video CV's [30] , and news article classification [31] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ro-man46459.2019.8956243">doi:10.1109/ro-man46459.2019.8956243</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ro-man/RoweSYBS19.html">dblp:conf/ro-man/RoweSYBS19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wqggnxazbrcypezoam76vj4q2i">fatcat:wqggnxazbrcypezoam76vj4q2i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210805051730/https://arxiv.org/pdf/2108.01254v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1e/a9/1ea9aa61e67ff3010a22499582188dc5be261be7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ro-man46459.2019.8956243"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Multimodal Classification: Current Landscape, Taxonomy and Future Directions [article]

William C. Sleeman IV, Rishabh Kapoor, Preetam Ghosh
<span title="2021-09-18">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We address these challenges by proposing a new taxonomy for describing such systems based on trends found in recent publications on multimodal classification.  ...  Multimodal classification research has been gaining popularity in many domains that collect more data from multiple sources including satellite imagery, biometrics, and medicine.  ...  Both sets of feature vectors were concatenated before using with the Random Forest classifier.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.09020v1">arXiv:2109.09020v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yagsbnxeefcpneqwgflrxxioqa">fatcat:yagsbnxeefcpneqwgflrxxioqa</a> </span>
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A Multimodal Approach to Predict Social Media Popularity [article]

Mayank Meghawat, Satyendra Yadav, Debanjan Mahata, Yifang Yin, Rajiv Ratn Shah, Roger Zimmermann
<span title="2018-07-16">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Next, in this paper, we propose a multimodal approach which exploits visual features (i.e., content information), textual features (i.e., contextual information), and social features (e.g., average views  ...  Multimodal information embedded in such posts could be useful in predicting their popularity.  ...  Finally, in our multimodal approach, we combine all features derived from Applying Random Forest on Contextual and Social (CON-SOC) Information Random Forest is among the most widely used machine learning  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.05959v1">arXiv:1807.05959v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lj5vj42uvvejdm2ajhkrqjmnuq">fatcat:lj5vj42uvvejdm2ajhkrqjmnuq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191027145041/https://arxiv.org/pdf/1807.05959v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7c/6d/7c6d69855efc27ce5138114b171e2279dc2e3dab.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.05959v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Comparing supervised and unsupervised approaches to multimodal emotion recognition

Marcos Fernández Carbonell, Magnus Boman, Petri Laukka
<span title="2021-12-24">2021</span> <i title="PeerJ"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zs2czkfyybggbpvr26rbyxpsjy" style="color: black;">PeerJ Computer Science</a> </i> &nbsp;
Best performance (AUC = 0.88) was obtained by merging the output from the best unimodal vocal (Elastic Net, AUC = 0.82) and facial (Random Forest, AUC = 0.80) classifiers using a late fusion approach and  ...  Multimodal feature patterns for each emotion are described in terms of the vocal and facial features that contributed most to classifier performance.  ...  (Facial Action Units and functionals) for classification of each emotion for the best performing video classifier (random forest).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7717/peerj-cs.804">doi:10.7717/peerj-cs.804</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35036530">pmid:35036530</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8725659/">pmcid:PMC8725659</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h6f65qpiz5b45na6kdctsmyiie">fatcat:h6f65qpiz5b45na6kdctsmyiie</a> </span>
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Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network [article]

Mohammadreza Soltaninejad, Lei Zhang, Tryphon Lambrou, Nigel Allinson, Xujiong Ye
<span title="2017-04-26">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The results show that the application of the random forest classifier to multimodal MRI images using machine-learned features based on FCN and hand-designed features based on textons provides promising  ...  The score map with pixel-wise predictions is used as a feature map which is learned from multimodal MRI train-ing dataset using the FCN.  ...  However, when the model is created, both FCN and RF are fast to use for classification of new datasets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1704.08134v1">arXiv:1704.08134v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7mqydo4snrclrawbo426th7rkq">fatcat:7mqydo4snrclrawbo426th7rkq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200927055157/https://arxiv.org/ftp/arxiv/papers/1704/1704.08134.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/50/43/50432703c89002134deff6a07691b5cc4a53a694.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1704.08134v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

Muhammad Naveed Iqbal Qureshi, Jooyoung Oh, Dongrae Cho, Hang Joon Jo, Boreom Lee
<span title="2017-09-08">2017</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/w7oms7cvvjfs7ht4yodsyhuzku" style="color: black;">Frontiers in Neuroinformatics</a> </i> &nbsp;
analysis (LDA), and random forest bagged tree classifier] by applying multimodal brain features and the proposed feature concatenation method.  ...  , and random forest ensemble classifiers.  ...  Copyright © 2017 Qureshi, Oh, Cho, Jo and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fninf.2017.00059">doi:10.3389/fninf.2017.00059</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28943848">pmid:28943848</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5596100/">pmcid:PMC5596100</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u6f4sgn4p5gfjjyyhzycjsz6ku">fatcat:u6f4sgn4p5gfjjyyhzycjsz6ku</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200206215714/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5596100&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6f/47/6f47559d16b3a07c33a9cb8e84465cf2cf85a5d3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fninf.2017.00059"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5596100" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Interactive pattern analysis for relevance feedback in multimedia information retrieval

Yimin Wu, Aidong Zhang
<span title="2004-06-01">2004</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/c2kkfk736jeeje2nj5asysaflq" style="color: black;">Multimedia Systems</a> </i> &nbsp;
To perform interactive pattern analysis, we propose two online pattern classification methods termed interactive random forests (IRF) and adaptive random forests (ARF), which adapt a composite classifier  ...  learning techniques called dynamic feature extraction and adaptive sample selection.  ...  To address multimodality, we develop two online and nonparametric pattern classification algorithms using random forests [4] , which trains a composite classifier from multiple tree classifiers.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00530-004-0136-5">doi:10.1007/s00530-004-0136-5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bczpv6kiwfayjgpxggyzumxspe">fatcat:bczpv6kiwfayjgpxggyzumxspe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20100617035305/http://www.cse.buffalo.edu/DBGROUP/psfiles/yiminkwu/acmmmsysj.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/dd/16/dd16733f350d6f20e7bd5dbfe6fe4249010a2bde.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00530-004-0136-5"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Classifying Diagrams and Their Parts using Graph Neural Networks: A Comparison of Crowd-Sourced and Expert Annotations [article]

Tuomo Hiippala
<span title="2019-12-05">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This article reports on two experiments that evaluate how effectively crowd-sourced and expert-annotated graphs can represent the multimodal structure of diagrams for representation learning using various  ...  This article compares two multimodal resources that consist of diagrams which describe topics in elementary school natural sciences.  ...  for graph classification using dummy (D), random forest (RF; 100 estimators) and support vector machine (SVM; C = 1.0) classifiers with balanced class weights.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.02866v1">arXiv:1912.02866v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u4snprup2jhkvazmoew5rhibf4">fatcat:u4snprup2jhkvazmoew5rhibf4</a> </span>
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A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning

Juan Zhou, Linfeng Hu, Yu Jiang, Liyue Liu, Min Tang
<span title="2021-02-09">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/icbhosh775h7bgzgot6avm3cua" style="color: black;">BioMed Research International</a> </i> &nbsp;
The SNP feature, ROI feature, and SNP-ROI joint feature were input into the deep learning model and trained by cross-validation technique.  ...  In order to improve the disease diagnosis performance of deep learning, we use the deep learning model to integrate SNP characteristics and ROI characteristics.  ...  Using the weight ranking results generated by the random forest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/8890513">doi:10.1155/2021/8890513</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33628827">pmid:33628827</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7886593/">pmcid:PMC7886593</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x3yrm5jw5bdediawmmm4zinfse">fatcat:x3yrm5jw5bdediawmmm4zinfse</a> </span>
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To Trust, or Not to Trust? A Study of Human Bias in Automated Video Interview Assessments [article]

Chee Wee Leong, Katrina Roohr, Vikram Ramanarayanan, Michelle P. Martin-Raugh, Harrison Kell, Rutuja Ubale, Yao Qian, Zydrune Mladineo, Laura McCulla
<span title="2019-11-27">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
that use them as training data.  ...  Specifically, we investigate whether human ratings themselves can be trusted at their face value when scoring video-based structured interviews, and whether such ratings can impact machine learning models  ...  Feature importance: We again compute feature importances (Random Forest, n=500), this time using all available features.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.13248v1">arXiv:1911.13248v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4f5udgl54rejposke4g3qoj654">fatcat:4f5udgl54rejposke4g3qoj654</a> </span>
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A Multimodal Music Emotion Classification Method Based on Multifeature Combined Network Classifier

Changfeng Chen, Qiang Li
<span title="2020-08-01">2020</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wpareqynwbgqdfodcyhh36aqaq" style="color: black;">Mathematical Problems in Engineering</a> </i> &nbsp;
The model uses multiple convolution kernels in CNN for 2D feature extraction, BiLSTM (bidirectional LSTM) for serialization processing and is used, respectively, for audio and lyrics single-modal emotion  ...  In the lyrics feature extraction, the chi-squared test vector and word embedding extracted by Word2vec are, respectively, used as the feature representation of the lyrics.  ...  Seo and Huh [7] used different classification algorithms such as random forest, DNN, and K-nearest neighbor for comparative analysis and used SVM as the best classification method.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2020/4606027">doi:10.1155/2020/4606027</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ptr36fiiavhb7gerhzcf5z5fpe">fatcat:ptr36fiiavhb7gerhzcf5z5fpe</a> </span>
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Random forest-based similarity measures for multi-modal classification of Alzheimer's disease

Katherine R. Gray, Paul Aljabar, Rolf A. Heckemann, Alexander Hammers, Daniel Rueckert
<span title="">2013</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a> </i> &nbsp;
Multimodality classification is then performed using coordinates from this joint embedding.  ...  Random forests provide consistent pairwise similarity measures for multiple modalities, thus facilitating the combination of different types of feature data.  ...  and Kelvin Leung (Dementia Research Centre, University College London) for provision of the MIDAS brain masks used for MRI anatomical segmentation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2012.09.065">doi:10.1016/j.neuroimage.2012.09.065</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/23041336">pmid:23041336</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3516432/">pmcid:PMC3516432</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ysrja6zfc5d35gr5dav42dwkl4">fatcat:ysrja6zfc5d35gr5dav42dwkl4</a> </span>
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