Keynote talks: Evolutionary feature selection and dimensionality reduction

Created by W.Langdon from gp-bibliography.bib Revision:1.7421

@InProceedings{Zhang:2017:APSIES,

• author = "Mengjie Zhang",
• title = "Keynote talks: Evolutionary feature selection and dimensionality reduction",
• booktitle = "2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES)",
• year = "2017",
• pages = "ix--xii",
• address = "Hanoi, Vietnam",
• month = "15-17 " # nov,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/IESYS.2017.8233551",
• size = "1 page",
• abstract = "In data mining and machine learning, many real-world problems such as bio-data classification and biomarker detection, image analysis, text mining often involve a large number of features/attributes. However, not all the features are essential since many of them are redundant or even irrelevant, and the useful features are typically not equally important. Using all the features for classification or other data mining tasks typically does not produce good results due to the big dimensionality and the large search space. This problem can be solved by feature selection to select a small subset of original (relevant) features or feature construction to create a smaller set of high-level features using the original low-level features.

  Feature selection and construction are very challenging tasks due to the large search space and feature interaction problems. Exhaustive search for the best feature subset of a given dataset is practically impossible in most situations. A variety of heuristic search techniques have been applied to feature selection and construction, but most of the existing methods still suffer from stagnation in local optima and/or high computational cost. Due to the global search potential and heuristic guidelines, evolutionary computation techniques such as genetic algorithms, genetic programming, particle swarm optimisation, ant colony optimisation, differential evolution and evolutionary multiobjective optimisation have been recently used for feature selection and construction for dimensionality reduction, and achieved great success. Many of these methods only select/construct a small number of important features, produce higher accuracy, and generated small models that are efficient on unseen data. Evolutionary computation techniques have now become an important means for handle big dimensionality and feature selection and construction.

  The talk will introduce the general framework within which evolutionary feature selection and construction can be studied and applied, sketching a schematic taxonomy of the field and providing examples of successful real-world applications. The application areas to be covered will include bio-data classification and biomarker detection, image analysis and object recognition and pattern classification, symbolic regression, network security and intrusion detection, and text mining. EC techniques to be covered will include genetic algorithms, genetic programming, particle swarm optimisation, differential evolution, ant colony optimisation, artificial bee colony optimisation, and evolutionary multi-objective optimisation. We will show how such evolutionary computation techniques can be effectively applied to feature selection/construction and dimensionality reduction and provide promising results.",

• notes = "Also known as \cite{8233551}",

}

Genetic Programming entries for Mengjie Zhang

Citations