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Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Machine learning

2019

Series

Zayed University

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Combining Machine Learning And Metaheuristics Algorithms For Classification Method Proaftn, Feras Al-Obeidat, Nabil Belacel, Bruce Spencer Jan 2019

Combining Machine Learning And Metaheuristics Algorithms For Classification Method Proaftn, Feras Al-Obeidat, Nabil Belacel, Bruce Spencer

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© Crown 2019. The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive …


A Hybrid Framework For Sentiment Analysis Using Genetic Algorithm Based Feature Reduction, Farkhund Iqbal, Jahanzeb Maqbool Hashmi, Benjamin C.M. Fung, Rabia Batool, Asad Masood Khattak, Saiqa Aleem, Patrick C.K. Hung Jan 2019

A Hybrid Framework For Sentiment Analysis Using Genetic Algorithm Based Feature Reduction, Farkhund Iqbal, Jahanzeb Maqbool Hashmi, Benjamin C.M. Fung, Rabia Batool, Asad Masood Khattak, Saiqa Aleem, Patrick C.K. Hung

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© 2019 IEEE. Due to the rapid development of Internet technologies and social media, sentiment analysis has become an important opinion mining technique. Recent research work has described the effectiveness of different sentiment classification techniques ranging from simple rule-based and lexicon-based approaches to more complex machine learning algorithms. While lexicon-based approaches have suffered from the lack of dictionaries and labeled data, machine learning approaches have fallen short in terms of accuracy. This paper proposes an integrated framework which bridges the gap between lexicon-based and machine learning approaches to achieve better accuracy and scalability. To solve the scalability issue that arises …