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Machine Learning Applications

  • Chapter
Springer Handbook of Computational Intelligence

Part of the book series: Springer Handbooks ((SHB))

Abstract

We describe the process of building computational intelligence (GlossaryTerm

CI

) models for machine learning (GlossaryTerm

ML

) applications. We use offline metaheuristics to design the models’ run-time architectures and online metaheuristics to control/aggregate the object-level models (base models) in these architectures. GlossaryTerm

CI

techniques complement more traditional statistical techniques, which are the core of GlossaryTerm

ML

for unsupervised and supervised learning. We analyze GlossaryTerm

CI

/GlossaryTerm

ML

industrial applications in the area of prognostics and health management (GlossaryTerm

PHM

) for industrial assets, and describe two GlossaryTerm

PHM

case studies. In the first case, we address anomaly detection for aircraft engines; in the second one, we rank locomotives in a fleet according to their expected remaining useful life. Then, we illustrate similar GlossaryTerm

CI

-enabled capabilities as they are applied to risk management for commercial and financial assets. In this context, we describe three case studies in insurance underwriting, mortgage collateral valuation, and portfolio optimization. We explain the current trend favoring the use of model ensemble and fusion over individual models, and emphasize the need for injecting diversity during the model generation phase. We present a model–agnostic fusion mechanism, which can be used with commoditized models obtained from crowdsourcing, cloud-based evolution, and other sources. Finally, we explore research trends, and future challenges/opportunities for GlossaryTerm

ML

techniques in the emerging context of big data and cloud computing.

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Abbreviations

AaaS:

analytics-as-a-service

AANN:

auto-associative neural network

AD:

anomaly detection

AI:

anomaly identification

AICOMP:

comparable based AI model

AIGEN:

generative AI model

ALM:

asset–liability management

BDAS:

Berkeley data analytics stack

BLB:

bag of little bootstrap

CART:

classification analysis and regression tree

CBR:

case-based reasoner

CF:

collaborative filtering

CI:

computational intelligence

CLM:

component level model

CSA:

contractual service agreement

DB:

database

DL:

deep learning

DSS:

decision support system

EA:

evolutionary algorithm

EC:

evolutionary computing

FIM:

fuzzy instance based model

FL:

fuzzy logic

FRC:

fuzzy-rule based classifier

GAOT:

genetic algorithm optimization toolbox

HM:

health management

HPC:

high-performance computing

ICA:

independent component analysis

IoT:

internet of things

LASSO:

least absolute shrinkage and selection operator

LDA:

linear discriminant analysis

LOCVAL:

locational value

LR:

logistic regression

M2M:

machine-to-machine

MAE:

mean of the absolute error

MARS:

multivariate adaptive regression splines

MDS:

multidimensional scaling

MH:

metaheuristic

ML:

machine learning

MOEA:

multiobjective evolutionary algorithm

MPI:

message passing interface

NC:

neural computation

NLPCA:

nonlinear principal components

NN:

neural network

NPV:

net present value

OEM:

original equipment manufacturer

OR:

operations research

PAC:

probably approximately correct

PCA:

principal component analysis

PC:

probabilistic computing

PHM:

prognostics and health management

PSEA:

Pareto sorting evolutionary algorithm

RBF:

radial basis function

RLP:

randomized linear programming

RL:

reinforcement learning

RUL:

remaining useful life

SC:

soft computing

SOM:

self-organizing map

SPEA:

strength Pareto evolutionary algorithm

SQL:

structured query language

SRD:

standard reference dataset

SR:

symbolic regression

SVaR:

simplified value at risk

SVM:

support vector machine

T2:

type-2

TGBF:

truncated generalized Bell function

TOGA:

target objective genetic algorithm

UW:

underwriter

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  1. Piero P. Bonissone Analytics, LLC, 3103 28th Street, CA 92104, San Diego, USA

    Piero P. Bonissone

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  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Bonissone, P.P. (2015). Machine Learning Applications. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_41

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