Preprocessing Perceptrons and Multivariate Decision Limits

Preprocessing Perceptrons and Multivariate Decision Limits

Patrik Eklund (Umeå University, Sweden) and Lena Kallin Westin (Umeå University, Sweden)
DOI: 10.4018/978-1-60566-218-3.ch005
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Classification networks, consisting of preprocessing layers combined with well-known classification networks, are well suited for medical data analysis. Additionally, by adjusting network complexity to corresponding complexity of data, the parameters in the preprocessing network can, in comparison with networks of higher complexity, be more precisely understood and also effectively utilised as decision limits. Further, a multivariate approach to preprocessing is shown in many cases to increase correctness rates in classification tasks. Handling network complexity in this way thus leads to efficient parameter estimations as well as useful parameter interpretations.
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The Preprocessing Perceptron

A linear regression is given by the weighted sum

where xi are inputs and wi and γ are parameters of the linear function. A logistic regression performs a sigmoidal activation of the weighted sum, i.e.,

Note that a logistic regression function is precisely a (single-layer) perceptron in the terminology of neural networks (Duda et. al., 2001). The preprocessing perceptron consists similarly of a weighted sum but includes preprocessing functions for each input variable. Suitable preprocessing functions are sigmoids (sigmoidal functions)

(1) where α is the parameter representing the position of the inflexion point, i.e., the soft cut-off or decision limit, and β corresponds1 to the slope value at the inflexion point. Often the sigmoid is also used as an activation function in neural networks. In this case, α is usually set 0 and β is set to 1, i.e.,


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Table of Contents
Riccardo Bellazzi
Petr Berka, Jan Rauch, Djamel Abdelkader Zighed
Petr Berka, Jan Rauch, Djamel Abdelkader Zighed
Chapter 1
Jana Zvárová, Arnošt Veselý
This chapter introduces the basic concepts of medical informatics: data, information, and knowledge. Data are classified into various types and... Sample PDF
Data, Information and Knowledge
Chapter 2
Michel Simonet, Radja Messai, Gayo Diallo
Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status... Sample PDF
Ontologies in the Health Field
Chapter 3
Alberto Freitas, Pavel Brazdil, Altamiro Costa-Pereira
This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to... Sample PDF
Cost-Sensitive Learning in Medicine
Chapter 4
Arnošt Veselý
This chapter deals with applications of artificial neural networks in classification and regression problems. Based on theoretical analysis it... Sample PDF
Classification and Prediction with Neural Networks
Chapter 5
Patrik Eklund, Lena Kallin Westin
Classification networks, consisting of preprocessing layers combined with well-known classification networks, are well suited for medical data... Sample PDF
Preprocessing Perceptrons and Multivariate Decision Limits
Chapter 6
Xiu Ying Wang, Dagan Feng
The rapid advance and innovation in medical imaging techniques offer significant improvement in healthcare services, as well as provide new... Sample PDF
Image Registration for Biomedical Information Integration
Chapter 7
ECG Processing  (pages 137-160)
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ECG Processing
Chapter 8
EEG Data Mining Using PCA  (pages 161-180)
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Chapter 9
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Chapter 14
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Chapter 15
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Mining Tuberculosis Data  (pages 332-349)
Marisa A. Sánchez, Sonia Uremovich, Pablo Acrogliano
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Chapter 17
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This chapter describes how to integrate medical knowledge with purely inductive (data-driven) methods for the creation of clinical prediction rules.... Sample PDF
Knowledge-Based Induction of Clinical Prediction Rules
Chapter 18
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