Automated synthesis of feature functions for pattern detection

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Guo:2010:CCECE,

• author = "Pei-Fang Guo and Prabir Bhattacharya and Nawwaf Kharma",
• title = "Automated synthesis of feature functions for pattern detection",
• booktitle = "23rd Canadian Conference on Electrical and Computer Engineering (CCECE), 2010",
• year = "2010",
• month = "2-5 " # may,
• abstract = "In pattern detection systems, the general techniques of feature extraction and selection perform linear transformations from primitive feature vectors to new vectors of lower dimensionality. At times, new extracted features might be linear combinations of some primitive features that are not able to provide better classification accuracy. To solve this problem, we propose the integration of genetic programming and the expectation maximisation algorithm (GP-EM) to automatically synthesise feature functions based on primitive input features for breast cancer detection. With the Gaussian mixture model, the proposed algorithm is able to perform nonlinear transformations of primitive feature vectors and data modelling simultaneously. Compared to the performance of other algorithms, such us the support vector machine, multi-layer perceptrons, inductive machine learning and logistic regression, which all used the entire primitive feature set, the proposed algorithm achieves a higher recognition rate by using one single synthesised feature function.",
• keywords = "genetic algorithms, genetic programming, Gaussian mixture model, automated synthesis, breast cancer detection, data modelling, expectation maximization algorithm, feature extraction, feature functions, inductive machine learning, logistic regression, multilayer perceptrons, pattern detection systems, primitive feature vector nonlinear transformations, support vector machine, cancer, data models, expectation-maximisation algorithm, feature extraction, medical computing, object detection, pattern classification, vectors",
• DOI = "doi:10.1109/CCECE.2010.5575224",
• ISSN = "0840-7789",
• notes = "Pei-Fang Guo PhD A Gaussian Mixture-Based Approach to Synthesizing Nonlinear Feature Functions for Automated Object Detection

  Concordia University 2010 http://users.encs.concordia.ca/~kharma/ResearchWeb/html/people/graduate%20students.html#pf_guo \cite{PeiFang_Guo:thesis}

  Also known as \cite{5575224}",

}

Genetic Programming entries for Pei Fang Guo Prabir Bhattacharya Nawwaf Kharma

Citations