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Encoding a Categorical Independent Variable for Input to TerrSet’s Multi-Layer Perceptron

ISPRS international journal of geo-information, 2021-10, Vol.10 (10), p.686 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2220-9964 ;EISSN: 2220-9964 ;DOI: 10.3390/ijgi10100686

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  • Title:
    Encoding a Categorical Independent Variable for Input to TerrSet’s Multi-Layer Perceptron
  • Author: Evenden, Emily ; Pontius Jr, Robert Gilmore
  • Subjects: Algorithms ; categorical variable ; Continuity (mathematics) ; Dependent variables ; encoding ; Geology ; Independent variables ; land change modeler ; Learning algorithms ; Machine learning ; Methods ; Multi-Layer Perceptron ; Multilayer perceptrons ; Neural networks ; Population ; Population Evidence Likelihood ; Probability theory ; Sample Empirical Probability ; Variables
  • Is Part Of: ISPRS international journal of geo-information, 2021-10, Vol.10 (10), p.686
  • Description: The profession debates how to encode a categorical variable for input to machine learning algorithms, such as neural networks. A conventional approach is to convert a categorical variable into a collection of binary variables, which causes a burdensome number of correlated variables. TerrSet’s Land Change Modeler proposes encoding a categorical variable onto the continuous closed interval from 0 to 1 based on each category’s Population Evidence Likelihood (PEL) for input to the Multi-Layer Perceptron, which is a type of neural network. We designed examples to test the wisdom of these encodings. The results show that encoding a categorical variable based on each category’s Sample Empirical Probability (SEP) produces results similar to binary encoding and superior to PEL encoding. The Multi-Layer Perceptron’s sigmoidal smoothing function can cause PEL encoding to produce nonsensical results, while SEP encoding produces straightforward results. We reveal the encoding methods by illustrating how a dependent variable gains across an independent variable that has four categories. The results show that PEL can differ substantially from SEP in ways that have important implications for practical extrapolations. If users must encode a categorical variable for input to a neural network, then we recommend SEP encoding, because SEP efficiently produces outputs that make sense.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2220-9964
    EISSN: 2220-9964
    DOI: 10.3390/ijgi10100686
  • Source: ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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