Decision tree learning based feature evaluation and selection for image classification

Han Liu, Mihaela Cocea, Weili Ding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

In the big data era, machine learning has become an increasingly popular approach for data processing. Data could be in various forms, such as text, images, audios, videos and signals. The essence of machine learning is to learn any patterns from features of data. In the above types of data, the number of features is massively high, which could result in the presence of a large number
of irrelevant features. Most machine learning algorithms are sensitive to irrelevant features so effective evaluation and selection of features in machine learning tasks are highly important. Also, effective evaluation of features can also help identify which features are necessary to be extracted from unstructured data. In this paper, we focus on the processing of image features in classification tasks. In particular, we review two main types of feature selection
techniques, namely filter and wrapper. We also review several machine learning approaches that have been used popularly in image classification, and identify the limitations of these algorithms in terms of feature evaluation. An experimental study is reported showing the performance of C4.5 (a decision tree learning algorithm) and other popular algorithms (Naive Bayes, K Nearest Neighbours and Multi-layer Perceptron) on five image data sets from the UCI repository. Furthermore, we describe the nature of decision tree learning algorithms for analysing the capability of such algorithms in terms of feature evaluation in the training stage and for showing how rules extracted a decision tree can be used for evaluating features in the validation stage.
Original languageEnglish
Title of host publication2017 International Conference on Machine Learning and Cybernetics (ICMLC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages569-574
Number of pages6
ISBN (Electronic)978-1-5386-0408-3
ISBN (Print)978-1-5386-0406-9 , 978-1-5386-0409-0
DOIs
Publication statusPublished - 16 Nov 2017
EventThe 16th International Conference on Machine Learning and Cybernetics (ICMLC) - Ningbo, China
Duration: 9 Jul 201712 Jul 2017

Publication series

NameIEEE ICMLC Proceedings Series
PublisherIEEE
ISSN (Electronic)2160-1348

Conference

ConferenceThe 16th International Conference on Machine Learning and Cybernetics (ICMLC)
Country/TerritoryChina
CityNingbo
Period9/07/1712/07/17

Keywords

  • data mining
  • machine learning
  • image classification
  • decision tree learning
  • feature evaluation

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