Print this page Share

Pattern Recognition: A Quality of Data Perspective

ISBN: 978-1-119-30282-7
352 pages
April 2018
Pattern Recognition: A Quality of Data Perspective (111930282X) cover image


A new approach to the issue of data quality in pattern recognition

Detailing foundational concepts before introducing more complex methodologies and algorithms, this book is a self-contained manual for advanced data analysis and data mining. Top-down organization presents detailed applications only after methodological issues have been mastered, and step-by-step instructions help ensure successful implementation of new processes. By positioning data quality as a factor to be dealt with rather than overcome, the framework provided serves as a valuable, versatile tool in the analysis arsenal.

For decades, practical need has inspired intense theoretical and applied research into pattern recognition for numerous and diverse applications. Throughout, the limiting factor and perpetual problem has been data—its sheer diversity, abundance, and variable quality presents the central challenge to pattern recognition innovation. Pattern Recognition: A Quality of Data Perspective repositions that challenge from a hurdle to a given, and presents a new framework for comprehensive data analysis that is designed specifically to accommodate problem data.

Designed as both a practical manual and a discussion about the most useful elements of pattern recognition innovation, this book:

  • Details fundamental pattern recognition concepts, including feature space construction, classifiers, rejection, and evaluation
  • Provides a systematic examination of the concepts, design methodology, and algorithms involved in pattern recognition
  • Includes numerous experiments, detailed schemes, and more advanced problems that reinforce complex concepts
  • Acts as a self-contained primer toward advanced solutions, with detailed background and step-by-step processes
  • Introduces the concept of granules and provides a framework for granular computing

Pattern recognition plays a pivotal role in data analysis and data mining, fields which are themselves being applied in an expanding sphere of utility. By facing the data quality issue head-on, this book provides students, practitioners, and researchers with a clear way forward amidst the ever-expanding data supply. 

See More

Table of Contents


Part I: Fundamentals

Chapter 1: Pattern Recognition ? Feature Space Construction

1.1 Concepts

1.2 From Patterns to Features

1.3 Features Scaling

1.4 Evaluation and Selection of Features

1.5  Conclusions


Chapter 2: Pattern Recognition ? Classifiers

2.1 Concepts

2.2 Nearest Neighbors Classification Method

2.3 Support Vector Machines Classification Algorithm

2.4 Decision Trees in Classification Problems

2.5 Ensemble Classifiers

2.6 Bayes Classifiers

2.7 Conclusions


Chapter 3: Classification with Rejection Problem Formulation and an Overview

3.1 Concepts

3.2 Concept of Rejecting Architectures

3.3 Native Patterns Based Rejection

3.4 Rejection Option in the Dataset of Native Patterns: a Case Study

3.5 Conclusions



Chapter 4: Evaluating Pattern Recognition Problem

4.1 Evaluating Recognition with Rejection - Basic Concepts

4.2 Classification with Rejection with no Foreign Patterns

4.3 Classification with Rejection ? Local Characterization

4.4 Conclusions


Chapter 5: Recognition with Rejection ? Empirical Analysis

5.1 Experimental Results

5.2 Geometrical Approach

5.3 Conclusions


Part II: Advanced Topics: A Framework of Granular Computing

Chapter 6: Concepts and Notions of Information Granules

6.1 Information granularity and Granular Computing

6.2 Formal platforms of information granularity

6.3 Intervals and calculus of intervals

6.4 Calculus of fuzzy sets

6.5 Characterization of information granules: coverage and specificity

6.6 Matching information granules

6.7 Conclusions


Chapter 7: Information Granules: Fundamental Constructs

7.1 The principle of justifiable granularity

7.2 Information granularity as a design asset

7.3 Single-step and multi-step prediction of temporal data in time series               models

7.4 Development of granular models of higher type

7.5 Classification with granular patterns

7.6 Conclusions


Chapter 8: Clustering

8.1 Fuzzy C-Means clustering method

8.2 K-Means clustering algorithm

8.3 Augmented fuzzy clustering with clusters and variables weighting

8.4 Knowledge-based clustering

8.5 Quality of clustering results

8.6 Information granules and interpretation of clustering results

8.7 Hierarchical clustering

8.8 Information granules in privacy problem: a concept of microaggregation

8.9 Development of information granules of higher type

8.10 Experimental studies

8.11 Conclusions


Chapter 9: Quality of Data: Imputation and Data Balancing

9.1Data imputation: underlying concepts and key problems

9.2 Selected categories of imputation methods

9.3 Imputation with the use of information granules

9.4 Granular imputation with the principle of justifiable granularity

9.5 Granular imputation with fuzzy clustering

9.6 Data imputation in system modeling

9.7 Imbalanced data and their granular characterization

9.8 Conclusions


See More

Author Information

WLADYSLAW HOMENDA, MSc., PhD, DSc., is an Associate Professor with the Faculty of Mathematics and Information Science at the Warsaw University of Technology, Poland, and an Associate Professor with the Faculty of Economics and Informatics in Vilnius at the University of Białystok, Lithuania.

WITOLD PEDRYCZ is a Professor with the Systems Research Institute, Polish Academy of Sciences, Poland, and a Professor and Canada Research Chair in the Department of Electrical and Computer Engineering at the University of Alberta, Edmonton, Canada.

See More

Related Titles

Back to Top