Genetic programming for multiple-feature construction on high-dimensional classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8612

@Article{TRAN:2019:patcog,

• author = "Binh Tran and Bing Xue and Mengjie Zhang",
• title = "Genetic programming for multiple-feature construction on high-dimensional classification",
• journal = "Pattern Recognition",
• year = "2019",
• volume = "93",
• pages = "404--417",
• month = sep,
• keywords = "genetic algorithms, genetic programming, Feature construction, Classification, Class dependence, High-dimensional data",
• ISSN = "0031-3203",
• URL = "http://www.sciencedirect.com/science/article/pii/S0031320319301815",
• DOI = "10.1016/j.patcog.2019.05.006",
• abstract = "Data representation is an important factor in deciding the performance of machine learning algorithms including classification. Feature construction (FC) can combine original features to form high-level ones that can help classification algorithms achieve better performance. Genetic programming (GP) has shown promise in FC due to its flexible representation. Most GP methods construct a single feature, which may not scale well to high-dimensional data. This paper aims at investigating different approaches to constructing multiple features and analysing their effectiveness, efficiency, and underlying behaviours to reveal the insight of multiple-feature construction using GP on high-dimensional data. The results show that multiple-feature construction achieves significantly better performance than single-feature construction. In multiple-feature construction, using multi-tree GP representation is shown to be more effective than using the single-tree GP thanks to the ability to consider the interaction of the newly constructed features during the construction process. Class-dependent constructed features achieve better performance than the class-independent ones. A visualisation of the constructed features also demonstrates the interpretability of the GP-based FC approach, which is important to many real-world applications",

}

Genetic Programming entries for Binh Ngan Tran Bing Xue Mengjie Zhang

Citations