Term Space Partition-Based Feature Construction Approach

2016 ◽  
pp. 74-87
Keyword(s):  
Feature Construction ◽  
Space Partition

scholarly journals Bayesian Nonparametric Space Partitions: A Survey

2021 ◽  
Author(s):  
Xuhui Fan ◽  
Bin Li ◽  
Ling Luo ◽  
Scott A. Sisson
Keyword(s):  
Theoretical Foundation ◽  
Dimensional Space ◽  
Classification Trees ◽  
Future Research ◽  
Feature Construction ◽  
Bayesian Nonparametric ◽  
Comprehensive Review ◽  
Space Partition ◽  
Self Consistency

Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a D-dimensional space into a set of blocks, such that the data within the same block share certain kinds of homogeneity. BNSP models are applicable to many areas, including regression/classification trees, random feature construction, and relational modelling. This survey provides the first comprehensive review of this subject. We explore the current progress of BNSP research through three perspectives: (1) Partition strategies, where we review the various techniques for generating partitions and discuss their theoretical foundation, `self-consistency'; (2) Applications, where we detail the current mainstream usages of BNSP models and identify some potential future applications; and (3) Challenges, where we discuss current unsolved problems and possible avenues for future research.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

Feature construction and smote-based imbalance handling for multi-label learning

2021 ◽  
Vol 563 ◽  
pp. 342-357
Author(s):  
Nitin Kumar Mishra ◽  
Pramod Kumar Singh
Keyword(s):  
Feature Construction
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