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Scalable feature selection using ReliefF aided by locality‐sensitive hashing
Author(s) -
EirasFranco Carlos,
GuijarroBerdiñas Bertha,
AlonsoBetanzos Amparo,
Bahamonde Antonio
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22546
Subject(s) - computer science , locality sensitive hashing , scalability , dimensionality reduction , hash function , hyperparameter , artificial intelligence , data mining , feature selection , curse of dimensionality , k nearest neighbors algorithm , machine learning , pattern recognition (psychology) , hash table , database , computer security
Feature selection algorithms, such as ReliefF, are very important for processing high‐dimensionality data sets. However, widespread use of popular and effective such algorithms is limited by their computational cost. We describe an adaptation of the ReliefF algorithm that simplifies the costliest of its step by approximating the nearest neighbor graph using locality‐sensitive hashing (LSH). The resulting ReliefF‐LSH algorithm can process data sets that are too large for the original ReliefF, a capability further enhanced by distributed implementation in Apache Spark. Furthermore, ReliefF‐LSH obtains better results and is more generally applicable than currently available alternatives to the original ReliefF, as it can handle regression and multiclass data sets. The fact that it does not require any additional hyperparameters with respect to ReliefF also avoids costly tuning. A set of experiments demonstrates the validity of this new approach and confirms its good scalability.