Nature and biology inspired approach of classification towards reduction of bias in machine learning

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Abstract

Machine learning has become a powerful tool in real applications such as decision making, sentiment prediction and ontology engineering. In the form of learning strategies, machine learning can be specialized into two types: supervised learning and unsupervised learning. Classification is a special type of supervised learning task, which can also be referred to as categorical prediction. In other words, classification tasks involve predictions of the values of discrete attributes. Some popular classification algorithms include Naïve Bayes and K Nearest Neighbour. The above type of classification algorithms generally involves voting towards classifying unseen instances. In traditional ways, the voting is made on the basis of any employed statistical heuristics such as probability. In Naïve Bayes, the voting is made through selecting the class with the highest posterior probability on the basis of the values of all independent attributes. In K Nearest Neighbour, majority voting is usually used towards classifying test instances. This kind of voting is considered to be biased, which may lead to overfitting. In order to avoid such overfitting, this paper proposes to employ a nature and biology inspired approach of voting referred to as probabilistic voting towards reduction of bias. An extended experimental study is reported to show how the probabilistic voting can manage to effectively reduce the bias towards improvement of classification accuracy.
Original languageEnglish
Title of host publicationThe 15th International Conference on Machine Learning and Cybernetics (ICMLC)
PublisherIEEE
Pages588-593
Number of pages6
ISBN (Electronic)978-1509003907
DOIs
Publication statusPublished - 9 Mar 2017
Event15th International Conference on Machine Learning and Cybernetics - Adelaide, Australia, Jeju Island, Korea, Republic of
Duration: 10 Jul 201613 Jul 2016
http://www.icmlc.com/

Publication series

NameInternational Conference on Machine Learning and Cybernetics
PublisherIEEE
ISSN (Print)2160-133X

Conference

Conference15th International Conference on Machine Learning and Cybernetics
Abbreviated titleICMLC 2016
Country/TerritoryKorea, Republic of
CityJeju Island
Period10/07/1613/07/16
Internet address

Keywords

  • data mining
  • machine learning
  • Naïve Bayes
  • K Nearest Neighbour
  • probabilistic classification

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