Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification

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

Abstract

The key issues faced pertaining to collection of epidemic disease data for research and analysis purposes, is that it is often a time consuming and expensive process. This results in availability of sparse sample data from which we aim to develop prediction models. To address this sparse sample data issue, the research carried out in this paper presents novel Incremental Transductive methods. These are used to circumvent the data collection process by applying previously acquired data to provide precise and consistently confident labelling alternatives to the pro- cess of manually retrieving relevant data from areas of interest. We have conducted research and investigated various approaches for semi-supervised machine learning including Bayesian models in terms of reasoning for labelling data. Results in this paper have shown that using the proposed Incremental Transductive methods, we can consistently label instances of data with a class of vector density to a high degree of confidence. By applying the Liberal (LTA) and Strict (STA) Training Approaches, we provide a bespoke labelling and classification process as an alternative to standalone algorithms. All of the methods employed in this paper are components in the process aimed at reducing the proliferation of the Schistosomiasis disease and its effects.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages8
Publication statusE-pub ahead of print - 12 Sep 2016
EventThe Dragon 3 Symposium 2016 -
Duration: 12 Sep 2016 → …

Conference

ConferenceThe Dragon 3 Symposium 2016
Period12/09/16 → …

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Labeling
Learning systems
Labels
Availability

Keywords

  • Oncomelania Hupensis
  • Cumulative Training Approach
  • Data Imputation
  • Correlation Co-efficient
  • Co-efficient of Determination

Cite this

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title = "Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification",
abstract = "The key issues faced pertaining to collection of epidemic disease data for research and analysis purposes, is that it is often a time consuming and expensive process. This results in availability of sparse sample data from which we aim to develop prediction models. To address this sparse sample data issue, the research carried out in this paper presents novel Incremental Transductive methods. These are used to circumvent the data collection process by applying previously acquired data to provide precise and consistently confident labelling alternatives to the pro- cess of manually retrieving relevant data from areas of interest. We have conducted research and investigated various approaches for semi-supervised machine learning including Bayesian models in terms of reasoning for labelling data. Results in this paper have shown that using the proposed Incremental Transductive methods, we can consistently label instances of data with a class of vector density to a high degree of confidence. By applying the Liberal (LTA) and Strict (STA) Training Approaches, we provide a bespoke labelling and classification process as an alternative to standalone algorithms. All of the methods employed in this paper are components in the process aimed at reducing the proliferation of the Schistosomiasis disease and its effects.",
keywords = "Oncomelania Hupensis, Cumulative Training Approach, Data Imputation, Correlation Co-efficient, Co-efficient of Determination",
author = "Terence Fusco and Yaxin Bi and Wang, {Haiying / HY} and Fiona Browne",
year = "2016",
month = "9",
day = "12",
language = "English",
isbn = "978-92-9221-304-6",
booktitle = "Unknown Host Publication",

}

Fusco, T, Bi, Y, Wang, HHY & Browne, F 2016, Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification. in Unknown Host Publication. The Dragon 3 Symposium 2016, 12/09/16.

Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification. / Fusco, Terence; Bi, Yaxin; Wang, Haiying / HY; Browne, Fiona.

Unknown Host Publication. 2016.

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

TY - GEN

T1 - Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification

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Y1 - 2016/9/12

N2 - The key issues faced pertaining to collection of epidemic disease data for research and analysis purposes, is that it is often a time consuming and expensive process. This results in availability of sparse sample data from which we aim to develop prediction models. To address this sparse sample data issue, the research carried out in this paper presents novel Incremental Transductive methods. These are used to circumvent the data collection process by applying previously acquired data to provide precise and consistently confident labelling alternatives to the pro- cess of manually retrieving relevant data from areas of interest. We have conducted research and investigated various approaches for semi-supervised machine learning including Bayesian models in terms of reasoning for labelling data. Results in this paper have shown that using the proposed Incremental Transductive methods, we can consistently label instances of data with a class of vector density to a high degree of confidence. By applying the Liberal (LTA) and Strict (STA) Training Approaches, we provide a bespoke labelling and classification process as an alternative to standalone algorithms. All of the methods employed in this paper are components in the process aimed at reducing the proliferation of the Schistosomiasis disease and its effects.

AB - The key issues faced pertaining to collection of epidemic disease data for research and analysis purposes, is that it is often a time consuming and expensive process. This results in availability of sparse sample data from which we aim to develop prediction models. To address this sparse sample data issue, the research carried out in this paper presents novel Incremental Transductive methods. These are used to circumvent the data collection process by applying previously acquired data to provide precise and consistently confident labelling alternatives to the pro- cess of manually retrieving relevant data from areas of interest. We have conducted research and investigated various approaches for semi-supervised machine learning including Bayesian models in terms of reasoning for labelling data. Results in this paper have shown that using the proposed Incremental Transductive methods, we can consistently label instances of data with a class of vector density to a high degree of confidence. By applying the Liberal (LTA) and Strict (STA) Training Approaches, we provide a bespoke labelling and classification process as an alternative to standalone algorithms. All of the methods employed in this paper are components in the process aimed at reducing the proliferation of the Schistosomiasis disease and its effects.

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KW - Correlation Co-efficient

KW - Co-efficient of Determination

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