Hybrid Genetic Algorithms for Feature Selection

By Il-Seok Oh, Jin-Seon Lee, B. Moon. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004

952 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c2210141876fd647deba27d2a9f15a87de81984b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients (2018, methodology): “The feature extraction process is generally followed by a feature selection algorithm to choose the most relevant features having a higher discrimination power in order to reduce the dimensionality of the feature vector space [15], [16].”
Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection (2019, methodology): “For instance, the ﬁrst proposed feature selection method using hybrid metaheuristic was in 2004 [24] using local search methods and the GA algorithm.”
KEEL: a software tool to assess evolutionary algorithms for data mining problems (2008, background): “2002), feature selection (Oh et al. 2004), instance selection (Wilson and Martinez 2000) and missing values imputation (Batista and Monard 2003).”
Feature selection for text classification: A review (2018, methodology): “Most hybrid methods employ some sort of filter methods to select promising features at first, then apply wrapper methods to the obtained features, for example those methods in [104] and [68], which are designed for generic data though instead of for text corpus.”
ECG-based heartbeat classification for arrhythmia detection: A survey (2016, background): “State-of-art techniques for attribute selection, such as Genetic Algorithms (GA) [109,110] and particle swarm optimization (PSO) [111,112] can also provide promising results and should be better investigated in future works.”
Feature Selection for Clustering: A Review (2018, methodology): “Representative feature selection algorithms of hybrid model include: BBHFS [13], HGA [53].”
A review of feature selection methods with applications (2015, methodology): “While practically any combination of filter and wrapper can be used for constructing the hybrid methodology, several interesting methodologies were recently proposed, such as: fuzzy random forest based feature selection [29], hybrid genetic algorithms [30], hybrid ant colony optimization [31], or mi”
Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification (2017, methodology): “Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.”
Normalized Mutual Information Feature Selection (2009, positive): “In [25], hybrid GAs are proposed that include local search operators to improve the fine-tuning capabilities of simple GAs.”
COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements (2007, methodology): “In particular, we plan to investigate the use of both floating searching based and stochastic search based feature selection methods [37], [38].”
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning (2013, background): “Since GAs are relatively insensitive to noise, they have proven to be an excellent choice for a basis of a more robust feature selection strategy and ever since the pioneering work by Siedlecki and Sklansky (38), many published studies have shown the advantages of the GAs in feature selection or lea”
A hybrid genetic algorithm for feature selection wrapper based on mutual information (2007, methodology): “Therefore, to improve the fine-tuning capability and efficiency of simple GAs, some hybrid GAs for feature selection have been developed by incorporating the above optimization methods or heuristic algorithms [ 23 , 37].”