A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATA
Doreswamy, and U. M. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 5 (2/3):
01 - 21(August 2016)
Abstract
Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.
%0 Journal Article
%1 noauthororeditor
%A Doreswamy,
%A M, Umme Salma
%D 2016
%J International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI)
%K Bat Binary Breast Cancer Classification Data FNN mining
%N 2/3
%P 01 - 21
%T A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATA
%U https://aircconline.com/ijscai/V5N3/5316ijscai01.pdf
%V 5
%X Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.
@article{noauthororeditor,
abstract = {Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.},
added-at = {2021-12-07T07:55:40.000+0100},
author = {Doreswamy and M, Umme Salma},
biburl = {https://www.bibsonomy.org/bibtex/2e7689422150f18f98efb7a5d092f0f75/leninsha},
interhash = {bd8100653ea3859c5174869258468773},
intrahash = {e7689422150f18f98efb7a5d092f0f75},
journal = {International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI)},
keywords = {Bat Binary Breast Cancer Classification Data FNN mining},
month = {August},
number = {2/3},
pages = {01 - 21},
timestamp = {2021-12-07T07:55:40.000+0100},
title = {A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATA },
url = {https://aircconline.com/ijscai/V5N3/5316ijscai01.pdf},
volume = 5,
year = 2016
}