Feature Selection and Classification on Prostate Cancer Microarray Gene Expression Profile

Arslan, Mustafa Turan
Kalınlı, Adem


Feature Selection and Classification on Breast Cancer Microarray Gene Expression Profile
Arslan, Mustafa Turan; Kalınlı, Adem (null; 2016-09-18)
Feature Selection for Classification of Human Micro-Doppler
Gurbuz, Sevgi Zubeyde; Tekeli, Burkan; Karabacak, Cesur; Yüksel Turgut, Ayşe Melda (2013-01-01)
Dozens of features have been proposed for the use in a variety of human micro-Doppler classification problems, such as activity classification, target identification, and arm swing detection. However, the issues of how many features are truly required, which features should be selected, and whether or how this selection will vary depending upon human activity has not yet been rigorously addressed in the context of human micro-Doppler analysis. Moreover, most classification results are present for the case w...
Feature discovery and classification of Doppler umbilical artery blood flow velocity waveforms
Baykal, Nazife; Yalabık, N.; Erkmen, Aydan Müşerref (Elsevier BV, 1996-11)
Doppler umbilical artery blood flow velocity waveform measurements are used in perinatal surveillance for the evaluation of fetal condition. There is an ongoing debate on the predictive value of Doppler measurements concerning the critical effect of the selection of parameters for the interpretation of Doppler waveforms. In this paper, we describe how neural network methods can be used both to discover relevant classification features and subsequently to; classify Doppler umbilical artery blood flow velocit...
Feature reduction for gene regulatory network control
Tan, Mehmet; Polat, Faruk; Alhajj, Reda (2007-10-17)
Scalability is one of the most important issues in control problems, including the control of gene regulatory networks. In this paper we argue that it is possible to improve scalability of gene regulatory networks control by reducing the number of genes to be considered by the control policy; and consequently propose a novel method to estimate genes that are less important for control. The reported test results on real and synthetic data demonstrate the applicability and effectiveness of the proposed approach.
Feature extraction and classification of blood cells for an automated differential blood count system
ONGUN, GÜÇLÜ; Halıcı, Uğur; Leblebicioğlu, Mehmet Kemal; Atalay, Mehmet Volkan; Beksac, M; Beksac, S (2001-07-19)
The differential blood counter (DBC) system that we have developed is an attempt to automate the task performed manually by experts in routine. Feature extraction and classification are two important components of our automated system. In this paper, classification of blood cells using various approaches including neural network based classifiers and support vector machine are presented together with the features used in the classification.
Citation Formats
M. T. Arslan and A. Kalınlı, “Feature Selection and Classification on Prostate Cancer Microarray Gene Expression Profile,” 2016, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/80441.