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Pushing the Limits Against the No Free Lunch Theorem: Towards Building General-Purpose (GenP) Classification Systems

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Advances in Selected Artificial Intelligence Areas

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 24))

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Abstract

In this chapter, we provide an overview of the tools our research group is exploiting to build general-purpose (GenP) classification systems. Although the “no free lunch” (NFL) theorem claims, in effect, that generating a universal classifier is impossible, the goals of GenP systems are more modest in requiring little to no parameter tuning for performing competitively across a range of tasks within a domain or with specific data types, such as images, that span across several fields. The tools outlined here for building GenP systems include methods for building ensembles, matrix representations of data treated as images, deep learning approaches, data augmentation, and classification within dissimilarity spaces. Each of these tools is explained in detail and illustrated with a few examples taken from our work building GenP systems, which spans nearly fifteen years. We note both our successes and some of our limitations. This chapter ends by pointing out some developments in quantum computing and quantum-inspired algorithms that may allow researchers to push the limits hypothesized by the NFL theorem even further.

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Abbreviations

ACT:

Activation Layer

CBD:

Compact Binary Descriptor

CLASS:

Classification Layer

CLBP:

Complete LBP

CNN:

Convolutional Neural Network

CONV:

Convolutional Layer

DCT:

Discrete Cosine Transform

DT:

Decision Trees

FC:

Fully-Connected Layer

GenP:

General Purpose (classifier)

GOLD:

Gaussians of Local Descriptors

GWT:

Gabor Wavelet Transform

HASC:

Heterogeneous Auto-Similarities of Characteristics

IDE:

Input Decimated Ensemble

INC:

Inception Module

LBP:

Local Binary Pattern

LDA:

Linear Discriminant Analysis

LPQ:

Local Phase Quantization

LTP:

Local Ternary Pattern

ML:

Machine Learning

MRELBP:

Median Robust Extended LBP

NFL:

No Free Lunch (theorem)

PCA:

Principal Component Analysis

PCAN:

Principal Component Analysis Network

PDV:

Pixel Difference Vectors (generated in CBD)

POOL:

Pooling Layer

QC:

Quantum Computation

QI:

Quantum Inspired (algorithms)

RES:

Residual Layer

RF:

Rotation Forest

RICLBP:

Rotation Invariant Co-occurrence LBP

RLBP:

Rotated LBP

RS:

Random Subspace

STFT:

Short-Term Fourier Transform

SVM:

Support Vector Machine

TL:

Transfer Learning

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Author information

Authors and Affiliations

  1. University of Bologna, Campus di Cesena, Via Macchiavelli, 47521, Cesena, FC, Italy

    Alessandra Lumini

  2. Dipartimento Di Ingegneria Dell’Informazione, University of Padova (Padua), Padova, Italy

    Loris Nanni

  3. Department of Information Systems and Cybersecurity, Missouri State University, Springfield, MO, USA

    Sheryl Brahnam

Authors
  1. Alessandra Lumini
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  2. Loris Nanni
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  3. Sheryl Brahnam
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Correspondence to Sheryl Brahnam .

Editor information

Editors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  3. KES International, Shoreham-By-Sea, UK

    Lakhmi C. Jain

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Lumini, A., Nanni, L., Brahnam, S. (2022). Pushing the Limits Against the No Free Lunch Theorem: Towards Building General-Purpose (GenP) Classification Systems. In: Virvou, M., Tsihrintzis, G.A., Jain, L.C. (eds) Advances in Selected Artificial Intelligence Areas. Learning and Analytics in Intelligent Systems, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-030-93052-3_5

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