scholarly journals Ensemble Pruning for Text Categorization Based on Data Partitioning

2011 ◽  
pp. 352-361 ◽  
Author(s):  
Cagri Toraman ◽  
Fazli Can
Keyword(s):  
Text Categorization ◽  
Data Partitioning ◽  
Ensemble Pruning

scholarly journals Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Aytuğ Onan
Keyword(s):  
Text Categorization ◽  
Predictive Performance ◽  
Experimental Results ◽  
Biomedical Text ◽  
Topic Modelling ◽  
Multiple Classifier System ◽  
Ensemble Pruning ◽  
Classifier System ◽  
Diversity Measures ◽  
Multiple Classifier

Text mining is an important research direction, which involves several fields, such as information retrieval, information extraction, and text categorization. In this paper, we propose an efficient multiple classifier approach to text categorization based on swarm-optimized topic modelling. The Latent Dirichlet allocation (LDA) can overcome the high dimensionality problem of vector space model, but identifying appropriate parameter values is critical to performance of LDA. Swarm-optimized approach estimates the parameters of LDA, including the number of topics and all the other parameters involved in LDA. The hybrid ensemble pruning approach based on combined diversity measures and clustering aims to obtain a multiple classifier system with high predictive performance and better diversity. In this scheme, four different diversity measures (namely, disagreement measure, Q-statistics, the correlation coefficient, and the double fault measure) among classifiers of the ensemble are combined. Based on the combined diversity matrix, a swarm intelligence based clustering algorithm is employed to partition the classifiers into a number of disjoint groups and one classifier (with the highest predictive performance) from each cluster is selected to build the final multiple classifier system. The experimental results based on five biomedical text benchmarks have been conducted. In the swarm-optimized LDA, different metaheuristic algorithms (such as genetic algorithms, particle swarm optimization, firefly algorithm, cuckoo search algorithm, and bat algorithm) are considered. In the ensemble pruning, five metaheuristic clustering algorithms are evaluated. The experimental results on biomedical text benchmarks indicate that swarm-optimized LDA yields better predictive performance compared to the conventional LDA. In addition, the proposed multiple classifier system outperforms the conventional classification algorithms, ensemble learning, and ensemble pruning methods.

Influence of feature weight on text categorization performance of Bayesian classifier

2009 ◽  
Vol 28 (12) ◽  
pp. 3080-3083 ◽  
Author(s):  
Xiu-mei GAO ◽  
Fang CHEN ◽  
Feng-xi SONG ◽  
Zhong JIN
Keyword(s):  
Text Categorization ◽  
Bayesian Classifier ◽  
Feature Weight

Graph-based dynamic ensemble pruning for facial expression recognition

2019 ◽  
Vol 49 (9) ◽  
pp. 3188-3206 ◽  
Author(s):  
Danyang Li ◽  
Guihua Wen ◽  
Xu Li ◽  
Xianfa Cai
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Ensemble Pruning

A lazy feature selection method for multi-label classification

2021 ◽  
Vol 25 (1) ◽  
pp. 21-34
Author(s):  
Rafael B. Pereira ◽  
Alexandre Plastino ◽  
Bianca Zadrozny ◽  
Luiz H.C. Merschmann
Keyword(s):  
Feature Selection ◽  
Text Categorization ◽  
Feature Selection Method ◽  
Selection Method ◽  
Video Classification ◽  
Classification Problems ◽  
Class Label ◽  
New Feature ◽  
Feature Selection Techniques ◽  
Biomolecular Analysis

In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.

scholarly journals Attention-based hierarchical recurrent neural networks for MOOC forum posts analysis

2020 ◽  
Author(s):  
Nicola Capuano ◽  
Santi Caballé ◽  
Jordi Conesa ◽  
Antonio Greco
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Text Categorization ◽  
Online Courses ◽  
Recurrent Network ◽  
Subject Area ◽  
Conversational Agents ◽  
Collaborative Tools ◽  
Experimental Findings ◽  
Productive Talk

AbstractMassive open online courses (MOOCs) allow students and instructors to discuss through messages posted on a forum. However, the instructors should limit their interaction to the most critical tasks during MOOC delivery so, teacher-led scaffolding activities, such as forum-based support, can be very limited, even impossible in such environments. In addition, students who try to clarify the concepts through such collaborative tools could not receive useful answers, and the lack of interactivity may cause a permanent abandonment of the course. The purpose of this paper is to report the experimental findings obtained evaluating the performance of a text categorization tool capable of detecting the intent, the subject area, the domain topics, the sentiment polarity, and the level of confusion and urgency of a forum post, so that the result may be exploited by instructors to carefully plan their interventions. The proposed approach is based on the application of attention-based hierarchical recurrent neural networks, in which both a recurrent network for word encoding and an attention mechanism for word aggregation at sentence and document levels are used before classification. The integration of the developed classifier inside an existing tool for conversational agents, based on the academically productive talk framework, is also presented as well as the accuracy of the proposed method in the classification of forum posts.

scholarly journals Text categorization Performance examination Using Machine Learning Algorithms

2020 ◽  
Vol 981 ◽  
pp. 022044
Author(s):  
Bonthala Prabhanjan Yadav ◽  
Sukhaveerji Ghate ◽  
A Harshavardhan ◽  
G Jhansi ◽  
Komuravelly Sudheer Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Categorization ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Performance Examination
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