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Reliable Knowledge Discovery

  • Conference proceedings
  • © 2012

Overview

Editors:
  1. Honghua Dai

    1. , School of Information Technology, Deakin University, Burwood, Australia

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  2. James N. K. Liu

    1. , Computing, Hong Kong Polytechnic University, Hunghom, Hong Kong SAR

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  3. Evgueni Smirnov

    1. , Department of Knowledge Engineering, Maastricht University, Maastricht, Netherlands

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Table of contents (17 papers)

  1. Front Matter

    Pages i-xviii
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  2. Reliability Estimation

    1. Front Matter

      Pages 1-1
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    2. Transductive Reliability Estimation for Individual Classifications in Machine Learning and Data Mining

      • Matjaž Kukar
      Pages 3-27

    3. Estimating Reliability for Assessing and Correcting Individual Streaming Predictions

      • Pedro Pereira Rodrigues, Zoran Bosnić, João Gama, Igor Kononenko
      Pages 29-49

    4. Error Bars for Polynomial Neural Networks

      • Nikolay Nikolaev, Evgueni Smirnov
      Pages 51-66

  3. Reliable Knowledge Discovery Methods

    1. Front Matter

      Pages 67-67
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    2. Robust-Diagnostic Regression: A Prelude for Inducing Reliable Knowledge from Regression

      • Abdul Awal Md. Nurunnabi, Honghua Dai
      Pages 69-92

    3. Reliable Graph Discovery

      • Honghua Dai
      Pages 93-107

    4. Combining Version Spaces and Support Vector Machines for Reliable Classification

      • Evgueni Smirnov, Georgi Nalbantov, Ida Sprinkhuizen-Kuyper
      Pages 109-126

    5. Reliable Ticket Routing in Expert Networks

      • Gengxin Miao, Louise E. Moser, Xifeng Yan, Shu Tao, Yi Chen, Nikos Anerousis
      Pages 127-147

    6. Reliable Aggregation on Network Traffic for Web Based Knowledge Discovery

      • Shui Yu, Simon James, Yonghong Tian, Wanchun Dou
      Pages 149-159

    7. Sensitivity and Generalization of SVM with Weighted and Reduced Features

      • Yan-xing Hu, James N. K. Liu, Li-wei Jia
      Pages 161-182

    8. Reliable Gesture Recognition with Transductive Confidence Machines

      • Ida Sprinkhuizen-Kuyper, Louis Vuurpijl, Youri van Pinxteren
      Pages 183-200

  4. Reliability Analysis

    1. Front Matter

      Pages 201-201
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    2. Reliability in A Feature-Selection Process for Intrusion Detection

      • Hai Thanh Nguyen, Katrin Franke, Slobodan Petrović
      Pages 203-218

    3. The Impact of Sample Size and Data Quality to Classification Reliability

      • Honghua Dai
      Pages 219-226

    4. A Comparative Analysis of Instance-based Penalization Techniques for Classification

      • Georgi Nalbantov, Patrick Groenen, Evgueni Smirnov
      Pages 227-238

    5. Subsequence Frequency Measurement and its Impact on Reliability of Knowledge Discovery in Single Sequences

      • Min Gan, Honghua Dai
      Pages 239-255

  5. Reliability Improvement Methods

    1. Front Matter

      Pages 257-257
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    2. Improving Reliability of Unbalanced Text Mining by Reducing Performance Bias

      • Ling Zhuang, Min Gan, Honghua Dai
      Pages 259-268
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Keywords

About this book

Reliable Knowledge Discovery focuses on theory, methods, and techniques for RKDD, a new sub-field of KDD. It studies the theory and methods to assure the reliability and trustworthiness of discovered knowledge and to maintain the stability and consistency of knowledge discovery processes. RKDD has a broad spectrum of applications, especially in critical domains like medicine, finance, and military.

Reliable Knowledge Discovery also presents methods and techniques for designing robust knowledge-discovery processes. Approaches to assessing the reliability of the discovered knowledge are introduced. Particular attention is paid to methods for reliable feature selection, reliable graph discovery, reliable classification, and stream mining. Estimating the data trustworthiness is covered in this volume as well. Case studies are provided in many chapters.

Reliable Knowledge Discovery is designed for researchers and advanced-level students focused on computer science and electrical engineering as a secondary text or reference. Professionals working in this related field and KDD application developers will also find this book useful.

Editors and Affiliations

  • , School of Information Technology, Deakin University, Burwood, Australia

    Honghua Dai

  • , Computing, Hong Kong Polytechnic University, Hunghom, Hong Kong SAR

    James N. K. Liu

  • , Department of Knowledge Engineering, Maastricht University, Maastricht, Netherlands

    Evgueni Smirnov

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