Halife Kodaz

Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism

Abstract 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.

Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease

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.

The medical applications of attribute weighted artificial immune system (AWAIS): diagnosis of heart and diabetes diseases

In our previous work, we had been proposed a new artificial immune system named as Attribute Weighted Artificial Immune System (AWAIS) to eliminate the negative effects of taking into account of all attributes in calculating Euclidean distance in shape-space representation which is used in many network-based Artificial Immune Systems (AISs). This system depends on the weighting attributes with respect to their importance degrees in class discrimination. These weights are then used in calculation of Euclidean distances. The performance analyses were conducted in the previous study by using machine learning benchmark datasets. In this study, the performance of AWAIS was investigated for real world problems. The used datasets were medical datasets consisting of Statlog Heart Disease and Pima Indian Diabetes datasets taken from University of California at Irvine (UCI) Machine Learning Repository. Classification accuracies for these datasets were obtained through using 10-fold cross validation method. AWAIS reached 82.59% classification accuracy for Statlog Heart Disease while it obtained a classification accuracy of 75.87% for Pima Indians Diabetes. These results are comparable with other classifiers and give promising performance to AWAIS for that kind of problems.

A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization

This article presented a parallel metaheuristic algorithm based on the Particle Swarm Optimization (PSO) to solve global optimization problems. In recent years, many metaheuristic algorithms have been developed. The PSO is one of them is very effective to solve these problems. But PSO has some shortcomings such as premature convergence and getting stuck in local minima. To overcome these shortcomings, many variants of PSO have been proposed. The comprehensive learning particle swarm optimizer (CLPSO) is one of them. We proposed a better variation of CLPSO, called the parallel comprehensive learning particle swarm optimizer (PCLPSO) which has multiple swarms based on the master-slave paradigm and works cooperatively and concurrently. The PCLPSO algorithm was compared with nine PSO variants in the experiments. It showed a great performance over the other PSO variants in solving benchmark functions including their large scale versions. Besides, it solved extremely fast the large scale problems.

A new classification method for breast cancer diagnosis: feature selection artificial immune recognition system (FS-AIRS)

In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with a new approach, FS-AIRS (Feature Selection Artificial Immune Recognition System) algorithm that has an important place in classification systems and was developed depending on the Artificial Immune Systems. With this purpose, 683 data in the Wisconsin breast cancer dataset (WBCD) was used. In this study, differently from the studies in the literature related to this concept, firstly, the feature number of each data was reduced to 6 from 9 in the feature selection sub-program by means of forming rules related to the breast cancer data with the C4.5 decision tree algorithm. After separating the 683 data set with reduced feature number into training and test sets by 10 fold cross validation method in the second stage, the data set was classified in the third stage with AIRS and a quite satisfying result was obtained with respect to the classification accuracy compared to the other methods used for this classification problem.

A New Classifier Based on Attribute Weighted Artificial Immune System (AWAIS)

‘Curse of Dimensionality’ problem in shape-space representation which is used in many network-based Artificial Immune Systems (AISs) affects classification performance at a high degree. In this paper, to increase classification accuracy, it is aimed to minimize the effect of this problem by developing an Attribute Weighted Artificial Immune System (AWAIS). To evaluate the performance of proposed system, aiNet, an algorithm that have a considerably important place among network-based AIS algorithms, was used for comparison with our developed algorithm. Two artificial data sets used in aiNet, Two-spirals data set and Chainlin k data set were applied in the performance analyses, which led the results of classification performance by means of represented network units to be higher than aiNet. Furthermore, to evaluate performance of the algorithm in a real world application, wine data set that taken from UCI Machine Learning Repository is used. For the artificial data sets, proposed system reached 100% classification accuracy with only a few numbers of network units and for the real world data set, wine data set, the algorithm obtained 98.23% classification accuracy which is very satisfying result if it is considered that the maximum classification accuracy obtained with other systems is 98.9%.

Community detection from biological and social networks

Display Omitted We propose six metaheuristic optimization algorithms to solve the community detection (CD) problem.The proposed algorithms have been modified in order to use for solving modularity optimization problem which is a discrete optimization problem.The four algorithms (HDSA, BADE, SSGA and BB-BC) have been supported by new techniques or hybrid methods in addition to their original versions.Comparative analyses of the proposed algorithms are performed on the four biological and five social networks.According to acquired experimental results, it has been observed that HDSA is more efficient and competitive than the other algorithms. In order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big Bang-Big Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks - five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested.

Medical application of information gain-based artificial immune recognition system (IG-AIRS): Classification of microorganism species

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 is minimized 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, microorganism dataset was applied in the performance analysis of our proposed system. Microorganism dataset was obtained using Cyranose 320 electronic nose. Our proposed system reached 92.35% classification accuracy with five-fold cross validation method. This result ensured that IG-AIRS would be helpful in classification of microorganism species based on laboratory tests, and would open the way to various microorganism species determine support by using electronic nose.

Classification of internal carotid artery Doppler signals using fuzzy discrete hidden Markov model

Research highlights? We developed a biomedical system based on fuzzy discrete hidden Markov model (FDHMM) in order to classify the internal carotid artery Doppler signals. The system consists of feature extraction and classification stages. In the feature extraction stage, Burg autoregressive (AR) spectrum analysis technique was used in order to obtain medical information. In the classification stage, in order to avoid losing information due to vector quantization and to increase the classification performance, a fuzzy logic based approach was applied. Our proposed method reached 97.38% classification accuracy with 5-fold cross validation (CV) technique. The classification results showed that the FDHMM is effective for classification of internal carotid artery Doppler signals. We developed a biomedical system based on Discrete Hidden Markov Model (DHMM). The aim of our system is to classify the internal carotid artery Doppler signals. We applied a fuzzy approach to DHMM. Thus we decreased information loss and increased the classification performance. Our system reached 97.38% of classification accuracy with 5 fold cross validation. These results showed that the Fuzzy Discrete Hidden Markov Model (FDHMM) method is effective for classification of internal carotid artery Doppler signals.

Thyroid disease diagnosis using Artificial Immune Recognition System (AIRS)

The use of artificial intelligence methods in medical diagnosis is increasing gradually. The effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Artificial Immune Systems (AIS) is a new but effective branch of artificial intelligence. This study aims at diagnosing thyroid disease with Artificial Immune Recognition System (AIRS). Thyroid disease diagnosis is an important classification problem. The thyroid data employed in this study is available from UCI Repository site. This data set is a very commonly used data set in the literature relating the use of classification systems for thyroid disease diagnosis and it was used in this study to compare the classification performance of AIRS with regard to other studies. We obtained a classification accuracy of 94.82%, which is one of the highest accuracies reached so far. This result ensured that 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.