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

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

Erica Craig (Western Ecological Studies, USA) and Falk Huettmann (University of Alaska-Fairbanks, USA)
DOI: 10.4018/978-1-59904-982-3.ch004
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Abstract

The use of machine-learning algorithms capable of rapidly completing intensive computations may be an answer to processing the sheer volumes of highly complex data available to researchers in the field of ecology. In spite of this, the continued use of less effective, simple linear, and highly labor intensive techniques such as stepwise multiple regression continue to be widespread in the ecological community. Herein we describe the use of data-mining algorithms such as TreeNet and Random Forests (Salford Systems), which can rapidly and accurately identify meaningful patterns and relationships in subsets of data that carry various degrees of outliers and uncertainty. We use satellite data from a wintering Golden Eagle as an example application; judged by the consistency of the results, the resultant models are robust, in spite of 30 % faulty presence data. The authors believe that the implications of these findings are potentially far-reaching and that linking computational software with wildlife ecology and conservation management in an interdisciplinary framework cannot only be a powerful tool, but is crucial toward obtaining sustainability.
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Introduction

Individual species and even entire ecosystems are at risk because of climatic changes and destruction of native habitats that are occurring worldwide, simultaneous with increased pressures from the expansion of human populations (Bittner, Oakley, Hannan, Lincer, Muscolino, & Domenech, 2003; Braun, 2005; Knick, Dobkin, Rotenberry, Schroeder, Vander Haegen, & Van Riper, 2003; Millenium Ecosystem Assessment, 2005; Primack, 1998; Zakri, 2003). Knowing and understanding factors that affect species and even that drive entire systems is vital for assessing populations that are at risk, as well as for making land management decisions that promote species sustainability. Advances in geographic information system technology (GIS) and digital online data availability coupled with the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets that have the potential to provide the global community with valuable information for pursuing these goals. However, the sheer volume and complexity of such animal location data can overwhelm biologists charged with making resource management decisions (Huettmann, 2005 for data overview). Further, it can affect the ability to obtain accurate results and to find the best possible solutions for making sustainable decisions. These major obstacles often result in under-utilization of data. Not only is it difficult to accurately filter out erroneous animal locations, it is challenging to identify meaningful patterns from data with multi-dimensional input variables (Braumoeller, 2004). Traditional statistical regression methods are limited by the inability to truly meet the assumptions required for analysis, such as the distribution of variables, model fit, independence of variables and linearity of the data (James & McCulloch, 1990; Nielsen, Boyce, Stenhouse, & Munro, 2002). They also are incapable of explaining the relationships between response and predictor variables (De’ath 2007). However, researchers continue to use the very time consuming, general linear models which use stepwise multiple regression methods (e.g., Manly, McDonald, Thomas, McDonald, & Erickson, 2002; Nielsen et al., 2002) as the predominant analytical approach for such analyses (see Whittingham, Stephens, Bradbury, & Freckleton, 2006 for an assessment of use of these techniques in the ecological literature).

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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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