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A SVM-Based Ensemble Approach to Multi-Document Summarization
[chapter]
2009
Lecture Notes in Computer Science
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In this paper, we present a Support Vector Machine (SVM) based ensemble approach to combat the extractive multi-document summarization problem. ...
Although SVM can have a good generalization ability, it may experience a performance degradation through wrong classifications. We use a committee of several SVMs, i.e. ...
SVM Ensemble We use the cross-validation committees (CVC) approach to build a SVM ensemble. ...
doi:10.1007/978-3-642-01818-3_23
fatcat:enmh6yngf5dvfezrnsmuabb4bm
Mining Newsgroups Using Ensemble Classifiers in Social Network Analysis
2017
International Journal of Engineering Science Advanced Computing and Bio-Technology
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A Classifier ensemble is designed using Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers. ...
Document retrieval, categorization, routing and filtering systems are often based on text classification. Text Classification means allocating a document to one or more categories or classes. ...
., 2013) used an approach used to segment image document and classify the document regions as text, image, drawings and table. ...
doi:10.26674/ijesacbt/2017/49177
fatcat:h6b7nxrorva4ri6ooro6k343qu
Complex Question Answering: Homogeneous or Heterogeneous, Which Ensemble Is Better?
[chapter]
2014
Lecture Notes in Computer Science
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For the homogeneous ensemble, we employ Support Vector Machines (SVM) as the learning algorithm and use a Cross-Validation Committees (CVC) approach to form several base models. ...
We use SVM, Hidden Markov Models (HMM), Conditional Random Fields (CRF), and Maximum Entropy (MaxEnt) techniques to build different base models for the heterogeneous ensemble. ...
We use query-focused supervised extractive multi-document summarization technique for this purpose [1] [2] [3] . ...
doi:10.1007/978-3-319-07983-7_21
fatcat:2xmofdfk4bbebc5rarbo7meyiy
A New Pairwise Ensemble Approach for Text Classification
[chapter]
2003
Lecture Notes in Computer Science
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In this paper we present a new pairwise ensemble approach, which uses pairwise Support Vector Machine (SVM) classifiers as base classifiers and "input-dependent latent variable" method for model combination ...
Our experiments on two multi-genre collections and one topic-based classification datasets show that the pairwise ensemble method outperforms both boosting, which has been demonstrated as a powerful ensemble ...
Building pairwise classifier for multi-class classification problems is not a new idea and many attempts have been made to build ensemble approaches, such as ECOC [6] , pairwise coupling [13] , and round ...
doi:10.1007/978-3-540-39857-8_26
fatcat:sfazrf5jvbbtzblin2ld3hds24
A Novel Multi-View Ensemble Learning Architecture to Improve the Structured Text Classification
2022
Information
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Multi-view ensemble learning exploits the information of data views. ...
To test its efficiency for full text classification, a technique has been implemented where the views correspond to the document sections. ...
Conclusions and Future Work The main contributions of the paper can be summarized as follows: • We propose a novel, efficient multi-view ensemble classification scheme based on stacking. ...
doi:10.3390/info13060283
fatcat:jljg4sbgfrckfdiwp6ruyuvaya
Imbalanced Sentiment Classification with Multi-strategy Ensemble Learning
2011
2011 International Conference on Asian Language Processing
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To handle the imbalanced problem, we propose a multi-strategy ensemble learning approach to this problem. ...
Our ensemble approach integrates sampleensemble, feature-ensemble, and classifier-ensemble by exploiting multiple classification algorithms. ...
In particular, a multi-strategy ensemble learning approach is proposed to solve this problem. ...
doi:10.1109/ialp.2011.28
dblp:conf/ialp/WangLZLZ11
fatcat:oagahuyup5czlhkvx44suvkuwy
The effectiveness of homogenous ensemble classifiers for Turkish and English texts
2016
2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)
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Multivariate Bernoulli Naïve Bayes is preferred as a base classifier due to its superior classification performance compared to the success of the other single classifiers. ...
To carry out this, the same type of base classifiers but diversified training sets are used which is referred as homogenous ensembles. ...
Authors in [13] applied ensemble methods to multi-class text documents where each document can belong to more than on category. ...
doi:10.1109/inista.2016.7571854
dblp:conf/inista/KilimciAO16
fatcat:gg2xmmikhrbufmylbklspkrcfi
A Hybrid Ensemble Word Embedding based Classification Model for Multi-document Summarization Process on Large Multi-domain Document Sets
2021
International Journal of Advanced Computer Science and Applications
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In this work, a hybrid multi-domain glove word embedding model, multi-document clustering and classification model were implemented to improve the multi-document summarization process for multi-domain ...
Contextual text feature extraction and classification play a vital role in the multi-document summarization process. ...
process. 3) Implemented a hybrid multi-document Bayesian approach based document summarization process on large document sets. ...
doi:10.14569/ijacsa.2021.0120918
fatcat:vsnwimayzfehld7rn4p43lty2u
NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis
[article]
2017
arXiv
pre-print
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This paper describes our multi-view ensemble approach to SemEval-2017 Task 4 on Sentiment Analysis in Twitter, specifically, the Message Polarity Classification subtask for English (subtask A). ...
Our system is a voting ensemble, where each base classifier is trained in a different feature space. The first space is a bag-of-words model and has a Linear SVM as base classifier. ...
System Description The proposed system consists of a multi-view ensemble with three base classifiers with different text representation techniques (feature spaces), that is, all base classifiers are trained ...
arXiv:1704.02263v1
fatcat:glgamlgy6vh6baxjwetclmjehy
An Ensemble Based Classification Approach For Medical Images
2017
Zenodo
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Now-a-days more researchers are applying the ensemble learning algorithm for classification to obtain high accuracy in an effectual manner. ...
Ensemble classification is a classifier applied to improve the performance of the single classifiers by fusing the output of the individual classifier models. ...
Classification can be applied to databases, text documents, web documents, web based text documents, etc. ...
doi:10.5281/zenodo.891658
fatcat:cintk4ngdzah7eymf4azuj7zqm
A Zipf-Like Distant Supervision Approach for Multi-document Summarization Using Wikinews Articles
[chapter]
2012
Lecture Notes in Computer Science
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This work presents a sentence ranking strategy based on distant supervision for the multi-document summarization problem. ...
Due to the difficulty of obtaining large training datasets formed by document clusters and their respective human-made summaries, we propose building a training and a testing corpus from Wikinews. ...
A support vector machine based ensemble approach is proposed in [3] . ...
doi:10.1007/978-3-642-34109-0_15
fatcat:w7k5ets52jga7psexzo6tfhlvu
Ensemble Detection of Single & Multiple Events at Sentence-Level
[article]
2014
arXiv
pre-print
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The low occurrence of multi-label sentences motivated the reduction of the hard imbalanced multi-label classification problem with low number of occurrences of multiple labels per instance to an more tractable ...
These new methods, such as the ensemble Chain of Classifiers, improve the F1 on average across the 6 labels by 2.8% over the Binary Relevance. ...
SINGLE-EVENT DETECTION A state-of-art way to solve a text multi-class problem, like single event detection, is to use SVM techniques [11] . ...
arXiv:1403.6023v1
fatcat:zeavfsmlvrfjrg6ajksr2qpsvm
Combining Active and Ensemble Learning for Efficient Classification of Web Documents
2014
POLIBITS Research Journal on Computer Science and Computer Engineering With Applications
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Classification of text remains a challenge. Most machine learning based approaches require many manually annotated training instances for a reasonable accuracy. ...
By passing only ambiguous instances to the human annotators the effort is reduced while maintaining a very good accuracy. ...
The base classifier on the left uses SVMs in a bagged ensemble. The specialized classifier on the right uses a single SVM. ...
doi:10.17562/pb-49-4
fatcat:36hkvm3q4nfbvi7e74gbarzorq
MCS for Online Mode Detection: Evaluation on Pen-Enabled Multi-touch Interfaces
2011
2011 International Conference on Document Analysis and Recognition
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On the more balanced multi-touch surface data set we achieved a recognition rate of close to 98 %. ...
This paper proposes a new approach for drawing mode detection in online handwriting. The system classifies groups of ink traces into several categories. ...
Some of these interfaces allow for pen-based interaction besides touch or multi-touch. ...
doi:10.1109/icdar.2011.194
dblp:conf/icdar/WeberLSSSD11
fatcat:q7xgm4beonf2vmg6aizc3jizkm
One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning
2021
Entropy
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In this research, three ensemble approaches were applied: accuracy as a weight ensemble, mean ensemble, and accuracy per class as a weight ensemble with a combination of four different DL models—long short-term ...
An advanced utilization of DL is the ensemble approach, which aims to reduce error rates and learning time and improve performance. ...
These SR classification systems are based on the ensemble approach, which was applied using DL models for the first-time. ...
doi:10.3390/e23101264
pmid:34681988
pmcid:PMC8535052
fatcat:cpak5ndbtjfproiaxq5imeyaky
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