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Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Polat, Kemal; Sahan, Seral; Guenes, Salih
It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of heart disease, which is a very common and important disease, was conducted with such a machine learning system. In this system, a new weighting scheme based on k-nearest neighbour (k-nn) method was utilized as a preprocessing step before the main classifier. Artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism was our used classifier. We took the dataset used in our study from the UCI Machine Learning Database. The obtained classification accuracy of our system was 87% and it was very promising with regard to the other classification applications in the literature for this problem. (C) 2006 Elsevier Ltd. All rights reserved.
Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Polat, Kemal; Sahan, Seral; Kodaz, Halife; Guenes, Salih
Artificial Immune Recognition System (AIRS) classification algorithm, which has an important place among classification algorithms in the field of Artificial Immune Systems, has showed an effective and intriguing performance on the problems it was applied. AIRS was previously applied to some medical classification problems including Breast Cancer, Cleveland Heart Disease, Diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by Fuzzy-Logic. This system, named as Fuzzy-AIRS was used as a classifier in the diagnosis of Breast Cancer and Liver Disorders, which are of great importance in medicine. The classifications of Breast Cancer and BUPA Liver Disorders datasets taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. Fuzzy-AIRS, which reached to classification accuracy of 98.51% for breast cancer, classified the Liver Disorders dataset with 83.36% accuracy. For both datasets, Fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site. Beside of this success, Fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. In the experiments, it was seen that the classification time in Fuzzy-AIRS was reduced about 70% of AIRS for both datasets. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, Fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems. (C) 2005 Elsevier Ltd. All rights reserved.
Breast cancer diagnosis using least square support vector machine
(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2007) Polat, Kemal; Guenes, Salih
The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. In this paper, breast cancer diagnosis was conducted using least square support vector machine (LS-SVM) classifier algorithm. The robustness of the LS-SVM is examined using classification accuracy, analysis of sensitivity and specificity, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 98.53% and it is very promising compared to the previously reported classification techniques. Consequently, by LS-SVM, the obtained results show that the used method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system. (c) 2006 Elsevier Inc. All rights reserved.
A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine
(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Polat, Kemal; Guenes, Salih; Arslan, Ahmet
The aim of this study is to diagnosis of diabetes disease, which is one of the most important diseases in medical field using Generalized Discriminant Analysis (GDA) and Least Square Support Vector Machine (LS-SVM). Also, we proposed a new cascade learning system based on Generalized Discriminant Analysis and Least Square Support Vector Machine. The proposed system consists of two stages. The first stage, we have used Generalized Discriminant Analysis to discriminant feature variables between healthy and patient (diabetes) data as pre-processing process. The second stage, we have used LS-SVM in order to classification of diabetes dataset. While LS-SVM obtained 78.21% classification accuracy using 10-fold cross validation, the proposed system called GDA-LS-SVM obtained 82.05% classification accuracy using 10-fold cross validation. The robustness of the proposed system is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 82.05% and it is very promising compared to the previously reported classification techniques. (c) 2006 Elsevier Ltd. All rights reserved.
Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform
(ELSEVIER SCIENCE INC, 2007) Polat, Kemal; Guenes, Salih
The aim of this study is to detect epileptic seizure in EEG signals using a hybrid system based on decision tree classifier and fast Fourier transform (FFT). The present study proposes a hybrid system with two stages: feature extraction using FFT and decision making using decision tree classifier. The detection of epileptiform, discharges in the electroencephalogram (EEG) is an important part in the diagnosis of epilepsy. All data set were obtained from EEG signals of healthy subjects and subjects suffering from epilepsy diseases. For healthy subjects is background EEG (scalp) with open eyes and for epileptic patients correspond to a seizure recorded in hippocampus (epileptic focus) with depth electrodes. The evolution of proposed system was conducted using k-fold cross-validation, classification accuracy, and sensitivity and specificity values. We have obtained 98.68% and 98.72% classification accuracies using 5- and 10-fold cross-validation. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system. (C) 2006 Elsevier Inc. All rights reserved.
Computer aided medical diagnosis system based on principal component analysis and artificial immune recognition system classifier algorithm
(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Polat, Kemal; Guenes, Salih
In this study, diagnosis of lung cancer, which is a very common and important disease, was conducted with computer aided medical diagnosis system based on principal component analysis and artificial immune recognition system. The approach system has two stages. In the first stage, dimension of lung cancer dataset that has 57 features is reduced to 4 features using principal component analysis. In the second stage, artificial immune recognition system (AIRS) was our used classifier. We took the lung cancer dataset used in our study from the UCI (from University of California, Department of Information and Computer Science) Machine Learning Database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem. (c) 2006 Elsevier Ltd. All rights reserved.
Effect of feature-type in selecting distance measure for an artificial immune system as a pattern recognizer
(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2008) Oezsen, Seral; Guenes, Salih
In designing an artificial immune system (AIS) for a problem domain, one must select a distance measure to find the affinity between system units and input data after determining a representation type. Euclidean distance is a commonly used distance measure in many proposed methods and is selected intuitively or due to simplicity of implementation. But this selection must be done carefully by considering the properties of problem domain. For example, most problems use data vectors with discrete, real-valued and nominal feature values. Whereas Euclidean distance can be used in this kind of problems, some other similarity measures designed for these hybrid vectors would give better results. To call attention of AIS designer to this point, we have tested three distance criteria which are Euclidean distance, Manhattan distance, and hybrid similarity measure on a simple AIS for the classification of two medical dataset taken from the UCI machine learning repository. One of the datasets, Statlog heart disease, contains nominal, discrete and real-valued vectors while the other one, BUPA liver disorders dataset, consists of purely real-valued vectors. For Statlog dataset, the best classification result was obtained with hybrid similarity measure as expected because this dataset consists of three-types of features while results for BUPA dataset were not different so much for the used measures, which is also an expected result considering the nature of this dataset. (C) 2007 Elsevier Inc. All rights reserved.
The effect of generalized discriminate analysis (GDA) to the classification of optic nerve disease from VEP signals
(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Gueven, Ayseguel; Polat, Kemal; Kara, Sadik; Guenes, Salih
In this paper, we have investigated the effect of generalized discriminate analysis (GDA) on classification performance of optic nerve disease from visual evoke potentials (VEPs) signals. The GDA method has been used as a pre-processing step prior to the classification process of optic nerve disease. The proposed method consists of two parts. First, GDA has been used as pre-processing to increase the distinguishing of optic nerve disease from VEP signals. Second, we have used the C4.5 decision tree classifier, Levenberg Marquart (LM) back propagation algorithm, artificial immune recognition system (AIRS), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Without GDA, we have obtained 84.37%, 93.75%, 75%, 76.56%, and 53.125% classification accuracies using C4.5 decision tree classifier, LM back propagation algorithm, AIRS, LDA, and SVM algorithms, respectively. With GDA, 93.75%, 93.86%, 81.25%, 93.75%, and 93.75% classification accuracies have been obtained using the above algorithms, respectively. These results show that the GDA pre-processing method has produced very promising results in diagnosis of optic nerve disease from VEP signals. (C) 2007 Elsevier Ltd. All rights reserved.
The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythemato-squamous diseases
(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2006) Polat, Kemal; Guenes, Salih
This paper presents a novel method for differential diagnosis of erythemato-squamous disease. The proposed method is based on fuzzy weighted pre-processing, k-NN (nearest neighbor) based weighted pre-processing, and decision tree classifier. The proposed method consists of three parts. In the first part, we have used decision tree classifier to diagnosis erythemato-squamous disease. In the second part, first of all, fuzzy weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified using decision tree classifier. In the third part, k-NN based weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified via decision tree classifier. The employed decision tree classifier, fuzzy weighted pre-processing decision tree classifier, and k-NN based weighted pre-processing decision tree classifier have reached to 86.18, 97.57, and 99.00% classification accuracies using 20-fold cross validation, respectively. (C) 2006 Elsevier Inc. All rights reserved.
An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease
(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2007) Polat, Kemal; Guenes, Salih
Diabetes occurs when a body is unable to produce or respond properly to insulin which is needed to regulate glucose (sugar). Besides contributing to heart disease, diabetes also increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. In this paper, we have detected on diabetes disease, which is a very common and important disease using principal component analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS). The aim of this study is to improve the diagnostic accuracy of diabetes disease combining PCA and ANFIS. The proposed system has two stages. In the first stage, dimension of diabetes disease dataset that has 8 features is reduced to 4 features using principal component analysis. In the second stage, diagnosis of diabetes disease is conducted via adaptive neuro-fuzzy inference system classifier. We took the diabetes disease dataset used in our study from the UCI (from Department of Information and Computer Science, University of California) Machine Learning Database. The obtained classification accuracy of our system was 89.47% and it was very promising with regard to the other classification applications in literature for this problem. (c) 2006 Elsevier Inc. All rights reserved.
Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation
(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2006) Polat, Kemal; Guenes, Salih
This paper presents a novel method for diagnosis of hepatitis disease. The proposed method is based on a hybrid method that uses feature selection (FS) and artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism. AIRS has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabets, liver disorders classification. By hybridizing FS and AIRS with fuzzy resource allocation mechanism, a method is obtained to solve this diagnosis problem via classifying. The robustness of this method with regard to sampling variations is examined using a cross-validation method. We used hepatitis disease dataset which is taken from UCI machine learning repository. We obtained a classification accuracy of 92.59%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. The obtained classification accuracy of our system was 92.59% and it was very promising with regard to the other classification applications in literature for this problem. Also, sensitivity, and specificity values for hepatitis disease dataset were obtained as 100 and 85%. (C) 2006 Elsevier Inc. All rights reserved.
An improved approach to medical data sets classification: artificial immune recognition system with fuzzy resource allocation mechanism
(WILEY, 2007) Polat, Kemal; Guenes, Salih
The artificial immune recognition system (AIRS) has been shown to be an efficient approach to tackling a variety of problems such as machine learning benchmark problems and medical classification problems. In this study, the resource allocation mechanism of AIRS was replaced with a new one based on fuzzy logic. The new system, named Fuzzy-AIRS, was used as a classifier in the classification of three well-known medical data sets, the Wisconsin breast cancer data set (WBCD), the Pima Indians diabetes data set and the ECG arrhythmia data set. The performance of the Fuzzy-AIRS algorithm was tested for classification accuracy, sensitivity and specificity values, confusion matrix, computation time and receiver operating characteristic curves. Also, the AIRS and Fuzzy-AIRS algorithms were compared with respect to the amount of resources required in the execution of the algorithm. The highest classification accuracy obtained from applying the AIRS and Fuzzy-AIRS algorithms using 10-fold cross-validation was, respectively, 98.53% and 99.00% for classification of WBCD; 79.22% and 84.42% for classification of the Pima Indians diabetes data set; and 100% and 92.86% for classification of the ECG arrhythmia data set. Hence, these results show that Fuzzy-AIRS can be used as an effective classifier for medical problems.
Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease
(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Kodaz, Halife; Oezsen, Seral; Arslan, Ahmet; Guenes, Salih
In this paper, we have made medical application of a new artificial immune system named the information gain based artificial immune recognition system (IG-AIRS) which minimizes the negative effects of taking into account all attributes in calculating Euclidean distance in shape-space representation which is used in many artificial immune systems. For medical data, thyroid disease data set was applied in the performance analysis of our proposed system. Our proposed system reached 95.90% classification accuracy with 10-fold CV method. This result ensured that IG-AIRS would be helpful in diagnosing thyroid function based on laboratory tests, and would open the way to various ill diagnoses support by using the recent clinical examination data, and we are actually in progress. (C) 2008 Elsevier Ltd. All rights reserved.
Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS)
(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2008) Latifoglu, Fatma; Polat, Kemal; Kara, Sadik; Guenes, Salih
In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting preprocessing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease. (C) 2007 Elsevier Inc. All rights reserved.
A New Approach to Diagnosing of Importance Degree of Obstructive Sleep Apnea Syndrome: Pairwise AIRS and Fuzzy-AIRS Classifiers
(SPRINGER, 2008) Polat, Kemal; Yosunkaya, Sebnem; Guenes, Salih
Artificial Immune Recognition System (AIRS) classifier algorithm is robust and effective in medical dataset classification applications such as breast cancer, heart disease, diabetes diagnosis etc. In our previous work, we have proposed a new resource allocation mechanism called fuzzy resource allocation in AIRS algorithm both to improve the classification accuracy and to decrease the computation time in classification process. Here, AIRS and Fuzzy-AIRS classifier algorithms and one against all approach have been combined to increase the classification accuracy of obstructive sleep apnea syndrome (OSAS) that is an important disease that influences both the right and the left cardiac ventricle. The OSAS dataset consists of four classes including of normal (25 subjects), mild OSAS (AHI (Apnea and Hypoapnea Index) =5-15 and 14 subjects), moderate OSAS (AHI < 15-30 and 18 subjects), and serious OSAS (AHI < 30 and 26 subjects). In the extracting of features that is characterized the OSAS disease, the clinical features obtained from Polysomnography used diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering from this disease have been used. The used clinical features are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. Even though AIRS and Fuzzy-AIRS classifiers have been used in the classifying multi-class problems, theirs classification performances are low in the case of multi-class classification problems. Therefore, we have used two classes in AIRS and Fuzzy-AIRS classifiers by means of one against all approach instead of four classes comprising the healthy subjects, mild OSAS, moderate OSAS, and serious OSAS. We have applied the AIRS, Fuzzy-AIRS, AIRS with one against all approach (Pairwise AIRS), and Fuzzy-AIRS with one against all approach (Pairwise Fuzzy-AIRS) to OSAS dataset. The obtained classification accuracies are 63.41%, 63.41%, 87.19%, and 84.14% using the above methods for 200 resources, respectively. These results show that the best method for diagnosis of OSAS is the combination of AIRS and one against all approach (Pairwise AIRS).
New Data Pre-processing on Assessing of Obstructive Sleep Apnea Syndrome: Line Based Normalization Method (LBNM)
(SPRINGER-VERLAG BERLIN, 2008) Akdemir, Bayram; Guenes, Salih; Yosunkaya, Sebnem
Sleep disorders are a very common unawareness illness among public. Obstructive Sleep Apnea Syndrome (OSAS) is characterized with decreased oxygen saturation level and repetitive upper respiratory tract obstruction episodes during full night sleep. In the present study, we have proposed a novel data normalization method called Line Based Normalization Method (LBNM) to evaluate OSAS using real data set obtained from Polysomnography device as a diagnostic tool in patients and clinically suspected of suffering OSAS. Here, we have combined the LBNM and classification methods comprising C4.5 decision tree classifier and Artificial Neural Network (ANN) to diagnose the OSAS. Firstly, each clinical feature in OSAS dataset is scaled by LBNM method in the range of [0,I]. Secondly, normalized OSAS dataset is classified using different classifier algorithms including C4.5 decision tree classifier and ANN, respectively. The proposed normalization method was compared with min-max normalization, z-score normalization, and decimal scaling methods existing in literature on the diagnosis of OSAS. LBNM has produced very promising results on the assessing of OSAS. Also, this method could be applied to other biomedical datasets.
A new feature selection method on classification of medical datasets: Kernel F-score feature selection
(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Polat, Kemal; Guenes, Salih
In this paper, we have proposed a new feature selection method called kernel F-score feature selection (KFFS) used as pre-processing step in the classification of medical datasets. KFFS consists of two phases. In the first phase, input spaces (features) of medical datasets have been transformed to kernel space by means of Linear (Lin) or Radial Basis Function (RBF) kernel functions. By this way, the dimensions of medical datasets have increased to high dimension feature space. In the second phase, the F-score values of medical datasets with high dimensional feature space have been calculated using F-score formula. And then the mean value of calculated F-scores has been computed. If the F-score value of any feature in medical datasets is bigger than this mean value, that feature will be selected. Otherwise, that feature is removed from feature space. Thanks to KFFS method, the irrelevant or redundant features are removed from high dimensional input feature space. The cause of using kernel functions transforms from non-linearly separable medical dataset to a linearly separable feature space. In this study, we have used the heart disease dataset, SPECT (Single Photon Emission Computed Tomography) images dataset, and Escherichia coli Promoter Gene Sequence dataset taken from UCI (University California, Irvine) machine learning database to test the performance of KFFS method. As classification algorithms, Least Square Support Vector Machine (LS-SVM) and Levenberg-Marquardt Artificial Neural Network have been used. As shown in the obtained results, the proposed feature selection method called KFFS is produced very promising results compared to F-score feature selection. (C) 2009 Elsevier Ltd. All rights reserved.
A new medical decision making system: Least square support vector machine (LSSVM) with Fuzzy Weighting Pre-processing
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Comak, Emre; Polat, Kemal; Guenes, Salih; Arslan, Ahmet
The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. This study aims at diagnosing Liver Disorder with a new hybrid machine learning method. By hybridizing LSSVM with Fuzzy Weighting Pre-processing, a method was obtained to solve this diagnosis problem via classifying Liver Disorder. Fuzzy Weighting Pre-processing stage was developed firstly in our study. This Liver Disorder dataset is a very commonly used dataset in literature relating the use of classification systems for Liver Disorder Diagnosis and it was used in this study to compare the classification performance of our proposed method with regard other studies. We obtained a classification accuracy of 94.29%, which is the highest one reached so far. This result is for Liver Disorder but it states that this method can be used confidently for other medical diseases diagnosis problems, too. (C) 2005 Elsevier Ltd. All rights reserved.
A new method based on cube algebra for the simplification of logic functions
(SPRINGER HEIDELBERG, 2007) Kahramanli, Sirzat; Guenes, Salih; Sahan, Seral; Basciftci, Fatih
In this study an Off-set based direct-cover minimization method for single-output logic functions is proposed represented in a sum-of-products form. To find the sufficient set of prime implicants including the given On-cube with the existing direct-cover minimization methods, this cube is expanded for one coordinate at a time. The correctness of each expansion is controlled by the way in which the cube being expanded intersects with all of K < 2(n) Off-cubes. If we take into consideration that the expanding of one cube has a polynomial complexity, then the total complexity of this approach can be expressed as O(n(p))O(2(n)), that is, the product of polynomial and exponential complexities. To obtain the complete set of prime implicants including the given On-cube, the proposed method uses Off-cubes expanded by this On-cube. The complexity of this operation is approximately equivalent to the complexity of an intersection of one On-cube expanded by existing methods for one coordinate. Therefore, the complexity of the process of calculating of the complete set of prime implicants including given On-cube is reduced approximately to O(n(p)) times. The method is tested on several different kinds of problems and on standard MCNC benchmarks, results of which are compared with ESPRESSO.
A novel approach to estimation of E-coli promoter gene sequences: Combining feature selection and least square support vector machine (FS_LSSVM)
(ELSEVIER SCIENCE INC, 2007) Polat, Kemal; Guenes, Salih
In this paper, we have investigated the real-world task of recognizing biological concepts in DNA sequences. Recognizing promoters in strings that represent nucleotides (one of A, G, T, or C) has been performed using a novel approach based on combining feature selection (FS) and least square support vector machine (LSSVM). Dimensionality of Escherichia coli promoter gene sequences dataset has 57 attributes and 106 samples including 53 promoters and 53 non-promoters. The proposed system consists of two parts. Firstly, we have used the FS process to reduce the dimensionality of E. coli promoter gene sequences dataset that has 57 attributes. So the dimensionality of this dataset has been reduced to 4 attributes by means of FS process. Secondly, LSSVM classifier algorithm has been run to estimation the E. coli promoter gene sequences. In order to show the performance of the proposed system, we have used the success rate, sensitivity and specificity analysis, 10-fold cross validation, and confusion matrix. Whilst only LSSVM classifier has been obtained 80% success rate using 10-fold cross validation, the proposed system has been obtained 100% success rate for same condition. These obtained results indicate that the proposed approach improve the success rate in recognizing promoters in strings that represent nucleotides. (C) 2007 Elsevier Inc. All rights reserved.

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