Machine learning research progress

504 Pages·2010·6.515 MB·English

by  Vogel, Mia, Peters, Hannah

M L
ACHINE EARNING
RESEARCH PROGRESS

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M L
ACHINE EARNING
RESEARCH PROGRESS

HANNAH PETERS

AND
MIA VOGEL
EDITORS

Nova Science Publishers, Inc.
New York
Copyright © 2010 by Nova Science Publishers, Inc.

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LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA

Machine learning research progress / Hannah Peters and Mia Vogel (editors).
p. cm.
ISBN 978-1-61470-199-6 (eBook)
1. Machine learning. I. Peters, Hannah, 1951- II. Vogel, Mia.
Q325.5.M343 2008
006.3'1--dc22
2008013806

Published by Nova Science Publishers, Inc. (cid:212) New York
CONTENTS

Preface vii

Chapter 1 Machine Learning Approaches in Promoter Sequence Analysis 1
N.T. Tung, E. Yang and I.P. Androulakis

Chapter 2 Recent Advances in Machine Learning for Financial Markets 31
Raymond Kosala and Richard Kumaradjaja

Chapter 3 A Review of Bankruptcy Prediction Models: the Machine 45
Learning Perspective
Chih-Fong Tsai

Chapter 4 Application of Learning Machines and Combinatorial Algorithms 61
in Water Resources Management and Hydrologic Sciences
Abedalrazq F. Khalil, Yasir H. Kaheil, Kashif M. Gill
and Mac McKee

Chapter 5 Machine Learning Techniques to Identify Marker Genes for 107
Diagnostic Classification of Microarrays
E.W. Lang, R. Schachtner, D. Herold, D. Lutter, Ph. Knollmüller,
F. Theis, A.M. Tomé, P. Gómez Vilda, C.G. Puntonet,
J.M. Gorriz-Saez, G. Schmitz and M. Stetter

Chapter 6 Using Ensemble of Classifiers in Bioinformatics 177
Loris Nanni and Alessandra Lumini

Chapter 7 Evolving Rules from Neural Networks Trained on Binary and 211
Continuous Data
Humar Kahramanli and Novruz Allahverdi

Chapter 8 Machine Learning for Acoustic Model Training in Automatic 233
Speech Recognition
Carsten Meyer and Hauke Schramm

Chapter 9 Machine Learning for Knowledge Derived from the Paucity 269
of Data
Fengming M. Chang
vi Contents

Chapter 10 Evaluating the Computational Requirements of Using SVM 299
Software to Train Data-Intensive Problems
Christopher S. Oehmen and Bobbie-Jo M. Webb-Robertson

Chapter 11 Reservoir Computing for Sensory Prediction and Classification in 323
Adaptive Agents
Cornelius Weber, Kazuhiro Masui, Norbert Michael Mayer,
Jochen Triesch and Minoru Asada

Chapter 12 Boosting Linear Graph Embedding for Dimensionality Reduction 339
Haixian Wang and Zuhong Lu

Chapter 13 Expert Networks with Mixed Continuous and Categorical Feature 355
Variables: A Location Modeling Approach
Shu-Kay Ng and Geoffrey J. McLachlan

Chapter 14 Applicability of Statistical Learning Algorithms for Predicting 369
Skin Friction Capacity of Driven Piles in Clay
Pijush Samui

Chapter 15 Learning Methods for Spam Filtering 383
El-Sayed M. El-Alfy

Chapter 16 Learning Support Vector Regression Models for Fast Radiation 427
Dose Rate Calculations
Krešimir Trontl, Tomislav Šmuc and Dubravko Pevec

Chapter 17 Is the Parts-Based Concept of NMF Relevant for Object 463
Recognition Tasks?
Ivan Bajla and Daniel Soukup

Index 473
PREFACE

As a broad subfield of artificial intelligence, machine learning is concerned with the
design and development of algorithms and techniques that allow computers to “learn.” At a
general level, there are two types of learning: inductive and deductive. Inductive machine
learning methods extract rules and patterns out of massive data sets. The major focus of
machine learning research is to extract information from data automatically by computational
and statistical methods. Hence, machine learning is closely related not only to data mining
and statistics but also theoretical computer science. This book presents new and important
research in this field.
Gene transcription is one of the main biological processes that govern an organism’s
response to external stimuli. Understanding the mechanism of gene regulation offers an
avenue with which to model this response. It has been hypothesized that one of the primary
mechanisms for gene regulation is via transcription factor binding in which a protein
(transcription factor) binds to certain sequences in the genome. Computationally, researchers
hope to identify both the promoter region as well as the sequence motifs which are bound by
transcription factors via analysis of genomic sequences. Machine learning methods make the
hypothesis that these sequences are drawn from some underlying but unknown patterns of
base-pairs, and are attractive due to their ability to process the large amounts of sequence and
experimental data present. The ability to isolate these sequences is predicated upon the large
amount of binding and expression data from experimental protocols such as Chip-Chip,
SELEX, microarray and GRIP. The authors review the basic background of promoter
structure and then focus on three popular aspects of promoter studies: promoter prediction,
promoter analysis and promoter modeling. Chapter 1 covers widely-used computation
techniques such as Gibbs sampling, MEME, phylogenetic foot-printing and position weight
matrices in promoter analysis, motif alignment and promoter modules in promoter modeling.
Finally, this chapter describes the use of these algorithms in an integrative sense to isolate
regulatory modules to rationalize the results of experimental data.
Decision making in financial market is a complex process. A wrong decision can cost
millions or even billions of dollars and cause financial catastrophe to the financial institution
that manages public funds. We have seen the potential and the promise of AI applications in
the financial market for quite a while. However, it is still unclear about the state of the art of
AI applications in the financial market today, particularly, with the advancement in machine
learning, intelligent agent, and Web 2.0 technologies. Chapter 2 provides a commentary on
the recent advances in machine learning, intelligent agent and other AI techniques in financial
viii Hannah Peters and Mia Vogel
market applications, on their successes or failures, and on the future challenges of the
machine learning and intelligent agents in the financial market. The authors observe that
utilizing richer data for forecasting market, and developing an integrated framework using
Agent-Based Model and Artificial Market for both understanding and forecasting the
financial markets would be very promising research areas to pursue.
For financial institutions, it is very important to have the ability to predict or forecast
business failure. Incorrect decision making is likely to cause financial distress or crises.
Bankruptcy prediction is a major research problem in the accounting and finance domain. In
other words, it would be very useful if financial institutions have a prediction model that is
able to predict whether or not the loan customers would be bankrupt. Using machine learning
techniques such as neural networks, decision trees, etc. to develop bankruptcy prediction
models has been extensively studied since the 1990s. This chapter reviews 27 related journal
papers in the period between 2000 and 2007 focusing on developing hybrid and ensemble
classifiers. Related studies are compared by their classifier design, datasets used and other
experimental setups. Current achievements and limitations in developing bankruptcy
prediction models by machine learning are present and discussed. A number of future
research directions are also provided in Chapter 3.
Contemporary and water resource engineering and management rely increasingly on
pattern recognition techniques that have the ability to capitalize on the unrelenting
accumulation of data that is made possible by modern information technology and remote
sensing methods. In response to the growing information needs of modern water systems,
advanced computational models and tools have been devised to identify and extract relevant
information from the mass of data that is now available. Chapter 4 presents innovative
applications from computational learning science within the fields of hydrology,
hydrogeology, hydroclimatology and water management. The success of machine learning is
evident from the growing number of studies involving the application of Artificial Neural
Networks (ANN), Support Vector Machines (SVM), Relevance Vector Machines (RVM) and
Locally Weighted Projection Regression (LWPR) to address various issues in hydrologic
sciences. The applications that will be discussed within the chapter employ the machine
learning techniques mentioned above for intelligent modeling of reservoir operations,
temporal downscaling of precipitation, spatial downscaling of soil moisture and
evapotranspiration, comparisons of various techniques for groundwater quality modeling, and
forecasting of chaotic time series behavior. Combinatorial algorithms to capture the intrinsic
complexities in the modeled phenomena and to overcome disparate scales are developed; for
example, learning machines have been coupled with geostatistical techniques, non-
homogenous hidden Markov models, wavelets, and evolutionary computing techniques. This
chapter does not intend to be exhaustive; it reviews the progress that has been made over the
past decade in the use of learning machines in applied hydrologic sciences and presents a
summary of future needs and challenges for further advancement of these methods.
Intelligent and efficient mathematical and computational tools are needed to analyze and
interpret the information content buried in large-scale gene expression patterns made
available by the recent development of microarray technology [27, 28]. Modern machine
learning techniques like Support Vector Machines (SVM) or matrix decomposition
techniques, like Principal Component Analysis (PCA), Singular Value Decomposition
(SVD), Independent Component Analysis (ICA) and NonnegativeMatrix Factorization
(NMF) provide new and efficient analysis tools that are currently explored in this area [76].