Genetic programming for simultaneous feature selection and classifier design

By Durga Prasad Muni, Nikhil Ranjan Pal, Jyotirmay Das. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2006

347 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/0adc82eeabe785db3c3e916740c28dc3c39566b3

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

1. Bing Xue
2. Mengjie Zhang
3. Qinghua Hu
4. Daren Yu
5. Congxin Wu
6. Jinfu Liu
7. Will N. Browne
8. Zongxia Xie

In Computational Theory and Mathematics

1. Qinghua Hu
2. Daren Yu
3. Jinfu Liu
4. Mengjie Zhang
5. Bing Xue
6. Zongxia Xie
7. Congxin Wu
8. Will N. Browne

Citations in the corpus

• Neighborhood classifiers (2008, background): “Therefore, feature subset selection is implicitly or explicitly conducted for some learning systems (Muni & Pal, 2006; Neumann, Schnorr, & Steidl, 2005; Quinlan, 1993).”
• Feature Selection With Harmony Search (2012, neutral): “Other algorithms use random search or heuristic strategies in an attempt to avoid such shortcomings; nature-inspired heuristics such as genetic algorithms (GAs) [29], [56], genetic programming [41], tabu search [21], simulated annealing [11], and particle swarm optimization (PSO) [53] are utilized t”
• Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation (2007, background): “Experiments show irrelative attributes will deteriorate the performance of the learning algorithms for the curse of dimensionality, increase training and test times [2] [3].”
• Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms (2014, methodology): “[18] develop a multi-tree GP algorithm for feature selection (GPmtfs) to simultaneously select a feature subset and design a classifier using the selected features.”
• Neighborhood rough set based heterogeneous feature subset selection (2008, background): “There are several viewpoints to categorize such techniques: filter, wrapper and embedded [21,41], unsupervised [20] and supervised [1,11–14,22], etc.”
• Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (2013, positive): “Muni et al. [27] develop a multitree GP algorithm for feature selection (GPmtfs) to simultaneously select a feature subset and design a classifier using the selected features.”
• A Survey on Evolutionary Computation Approaches to Feature Selection (2016, background): “[19] developed a wrapper feature selection model”