Automatic Intelligent Data Analysis

Automatic Intelligent Data Analysis

Martin Spott (Intelligent Systems Research Centre, UK) and Detlef Nauck (Intelligent Systems Research Centre, UK)
DOI: 10.4018/978-1-59904-982-3.ch001
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Abstract

This chapter introduces a new way of using soft constraints for selecting data analysis methods that match certain user requirements. It presents a software platform for automatic data analysis that uses a fuzzy knowledge base for automatically selecting and executing data analysis methods. In order to support business users in running data analysis projects the analytical process must be automated as much as possible. The authors argue that previous approaches based on the formalisation of analytical processes were less successful because selecting and running analytical methods is very much an experience-led heuristic process. The authors show that a system based on a fuzzy knowledge base that stores heuristic expert knowledge about data analysis can successfully lead to automatic intelligent data analysis.
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Automating Data Analysis

When we talk about data analysis in this chapter we refer to the task of discovering a relationship between a number of attributes and representing this relationship in form of a model. Typically, we are interested in determining the value of some attributes given some other attributes (inference) or in finding groups of attribute-value combinations (segmentation). In this context we will not consider describing parameters of attribute distributions or visualisation.

Models are typically used to support a decision making process by inferring or predicting the (currently unknown) values of some output attributes given some input attributes or by determining a group to which the currently observed data record possibly belongs to. In this scenario we expect a model to be as accurate as possible. Models also can be used to explain a relationship between attributes. In this scenario we want a model to be interpretable.

A model is created in a (machine) learning process, where the parameters of the models are adapted based on set of training data. The learning process can be controlled by a separate validation set to prevent over-generalisation on the training set. The model performance is finally tested on a different test set.

In business environments data and problem owners are typically domain experts, but not data analysis experts. That means they do not have the required knowledge to decide which type of model and learning algorithm to choose, how to set the parameters of the learning procedure, how to adequately test the learned model, and so on. In order to support this group of users, we have developed an approach for automating data analysis to some extent.

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Editorial Advisory Board
Table of Contents
Preface
Hsiao-Fan Wang
Acknowledgment
Hsiao-Fan Wang
Chapter 1
Martin Spott, Detlef Nauck
This chapter introduces a new way of using soft constraints for selecting data analysis methods that match certain user requirements. It presents a... Sample PDF
Automatic Intelligent Data Analysis
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Chapter 2
Hung T. Nguyen, Vladik Kreinovich, Gang Xiang
It is well known that in decision making under uncertainty, while we are guided by a general (and abstract) theory of probability and of statistical... Sample PDF
Random Fuzzy Sets: Theory & Applications
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Chapter 3
Gráinne Kerr, Heather Ruskin, Martin Crane
Microarray technology1 provides an opportunity to monitor mRNA levels of expression of thousands of genes simultaneously in a single experiment. The... Sample PDF
Pattern Discovery in Gene Expression Data
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Chapter 4
Erica Craig, Falk Huettmann
The use of machine-learning algorithms capable of rapidly completing intensive computations may be an answer to processing the sheer volumes of... Sample PDF
Using "Blackbox" Algorithms Such AS TreeNET and Random Forests for Data-Ming and for Finding Meaningful Patterns, Relationships and Outliers in Complex Ecological Data: An Overview, an Example Using G
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Chapter 5
Eulalia Szmidt, Marta Kukier
We present a new method of classification of imbalanced classes. The crucial point of the method lies in applying Atanassov’s intuitionistic fuzzy... Sample PDF
A New Approach to Classification of Imbalanced Classes via Atanassov's Intuitionistic Fuzzy Sets
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Chapter 6
Arun Kulkarni, Sara McCaslin
This chapter introduces fuzzy neural network models as means for knowledge discovery from databases. It describes architectures and learning... Sample PDF
Fuzzy Neural Network Models for Knowledge Discovery
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Chapter 7
Ivan Bruha
This chapter discusses the incorporation of genetic algorithms into machine learning. It does not present the principles of genetic algorithms... Sample PDF
Genetic Learning: Initialization and Representation Issues
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Chapter 8
Evolutionary Computing  (pages 131-142)
Thomas E. Potok, Xiaohui Cui, Yu Jiao
The rate at which information overwhelms humans is significantly more than the rate at which humans have learned to process, analyze, and leverage... Sample PDF
Evolutionary Computing
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Chapter 9
M. C. Bartholomew-Biggs, Z. Ulanowski, S. Zakovic
We discuss some experience of solving an inverse light scattering problem for single, spherical, homogeneous particles using least squares global... Sample PDF
Particle Identification Using Light Scattering: A Global Optimization Problem
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Chapter 10
Dominic Savio Lee
This chapter describes algorithms that use Markov chains for generating exact sample values from complex distributions, and discusses their use in... Sample PDF
Exact Markov Chain Monte Carlo Algorithms and Their Applications in Probabilistic Data Analysis and Inference
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Chapter 11
J. P. Ganjigatti, Dilip Kumar Pratihar
In this chapter, an attempt has been made to design suitable knowledge bases (KBs) for carrying out forward and reverse mappings of a Tungsten inert... Sample PDF
Design and Development of Knowledge Bases for Forward and Reverse Mappings of TIG Welding Process
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Chapter 12
Malcolm J. Beynon
This chapter considers the role of fuzzy decision trees as a tool for intelligent data analysis in domestic travel research. It demonstrates the... Sample PDF
A Fuzzy Decision Tree Analysis of Traffic Fatalities in the US
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Chapter 13
Dymitr Ruta, Christoph Adl, Detlef Nauck
In the telecom industry, high installation and marketing costs make it six to 10 times more expensive to acquire a new customer than it is to retain... Sample PDF
New Churn Prediction Strategies in the Telecom Industry
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Chapter 14
Malcolm J. Beynon
This chapter demonstrates intelligent data analysis, within the environment of uncertain reasoning, using the recently introduced CaRBS technique... Sample PDF
Intelligent Classification and Ranking Analyses Using CARBS: Bank Rating Applications
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Chapter 15
Fei-Chen Hsu, Hsiao-Fan Wang
In this chapter, we used Cumulative Prospect Theory to propose an individual risk management process (IRM) including a risk analysis stage and a... Sample PDF
Analysis of Individual Risk Attitude for Risk Management Based on Cumulative Prospect Theory
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Chapter 16
Francesco Giordano, Michele La Rocca, Cira Perna
This chapter introduces the use of the bootstrap in a nonlinear, nonparametric regression framework with dependent errors. The aim is to construct... Sample PDF
Neural Networks and Bootstrap Methods for Regression Models with Dependent Errors
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Chapter 17
Lean Yu, Shouyang Wang, Kin Keung Lai
Financial crisis is a kind of typical rare event, but it is harmful to economic sustainable development if occurs. In this chapter, a... Sample PDF
Financial Crisis Modeling and Prediction with a Hilbert-EMD-Based SVM Approachs
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Chapter 18
Chun-Jung Huang, Hsiao-Fan Wang, Shouyang Wang
One of the key problems in supervised learning is due to the insufficient size of the training data set. The natural way for an intelligent learning... Sample PDF
Virtual Sampling with Data Construction Analysis
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About the Contributors