A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis

Proper interpretation of the thyroid gland functional data is an important issue in the diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body’s metabolism. Thyroid hormone produced by the thyroid gland provides this. Production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism) defines the type of thyroid disease. Artificial immune systems (AISs) is a new but effective branch of artificial intelligence. Among the systems proposed in this field so far, artificial immune recognition system (AIRS), which was proposed by A. Watkins, has shown an effective and intriguing performance on the problems it was applied. This study aims at diagnosing thyroid disease with a new hybrid machine learning method including this classification system. By hybridizing AIRS with a developed Fuzzy weighted pre-processing, 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 thyroid disease dataset which is taken from UCI machine learning respiratory. We obtained a classification accuracy of 85%, which is the highest one reached so far. The classification accuracy was obtained via a 10-fold cross-validation.     

Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing

This paper presents a novel method for diagnosis of heart disease. The proposed method is based on a hybrid method that uses fuzzy weighted pre-processing and artificial immune recognition system (AIRS). Artificial immune recognition system has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, liver disorders classification. The robustness of the proposed method is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 96.30% and it is very promising compared to the previously reported classification techniques.     

An improved approach to medical data sets classification: artificial immune recognition system with fuzzy resource allocation mechanism

Abstract: 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.     

Research on hybrid feature selection method of power transformer based on fuzzy information entropy

The data of dissolved gas in oil analysis (DGA) is uncertain affected by the influence of transformer capacity and fault location, which makes transformer fault diagnosis model based on DGA has low accuracy. Therefore, we propose a hybrid feature selection method based on fuzzy information entropy, whereby optimizing the reasonable DGA feature parameter according to the feature information between the parameter and fault type, to reduce the influence of DGA data uncertainty on the fault diagnosis accuracy. Firstly, the characteristic relevance and redundancy functions are constructed based on fuzzy information entropy theory. Secondly, these functions are taken as the optimization objectives of binary-chaotic multi-objective particle swarm optimization algorithm(B-CMOPSO), to search for the feature subsets in the feature space composed of 46 DGA feature parameters. Then, the optimal feature subset is selected based on the simulation accuracy of ELM, SVM, Adaboost.M1 and BPNN on the feature subsets. Finally, 30 simulation experiments are carried out to compare with several multi-objective optimization algorithms, common Filter methods and common DGA feature combinations, and the rationality of the proposed method is verified by the t-test method. The results show that the 4 classifiers accuracy means is maximatily improved by 18.95%, 20.77%, 19.85% and 21.27% respectively compared with common DGA feature combinations, indicating that the optimal feature subset preserves more feature information and can effectively reduce the influence of DGA data uncertainty on diagnostic accuracy.     

A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases

The use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems has improved in a great deal to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system (AIS) based fuzzy k-NN algorithm to determine the heart valve disorders from the Doppler heart sounds. The proposed methodology is composed of three stages. The first stage is the pre-processing stage. The feature extraction is the second stage. During feature extraction stage, Wavelet transforms and short time Fourier transform were used. As next step, wavelet entropy was applied to these features. In the classification stage, AIS based fuzzy k-NN algorithm is used. To compute the correct classification rate of proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters. 95.9% sensitivity and 96% specificity rate was obtained.     

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

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.     

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

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.     

Ovarian cancer diagnosis using a hybrid intelligent system with simple yet convincing rules

Ovarian cancer is the ninth most common cancer among women and ranks fifth in cancer deaths. Statistics show that the five-year survival rate is greater than 75% if diagnosis occurs before the cancer cells have spread to other organs (stage I), but it drops to 20% when the cancer cells have spread to upper abdomen (stage III). Therefore, it is crucial to detect ovarian cancer as early as possible and to correctly identify the stage of the cancer to prevent any further delay of appropriate treatments. In this paper, we propose a novel self-organizing neural fuzzy inference system that functions as a reliable decision support system for ovarian cancer diagnoses. The system only requires a limited number of control parameters and constraints to derive simple yet convincing inference rules without human intervention and expert guidance. Because feature selection and attribute reduction are performed during training, the inference rules possess a great level of interpretability. Experiments are conducted on both established medical data sets and real-world cases collected from hospital. The experimental results of our proposed model in ovarian cancer diagnoses are encouraging because it achieves the most number of correct diagnoses when benchmarked against other computational intelligence based models. More importantly, its automatically derived rules are consistent with expert knowledge.     

A new wind power prediction method based on ridgelet transforms,hybrid feature selection and closed-loop forecasting

To reduce network integration and boost energy trading, wind power forecasting can play an important role in power systems. Furthermore, the uncertain and nonconvex behavior of wind signals make its prediction complex. For this purpose, accurate prediction tools are needed. In this paper, a ridgelet transform is applied to a wind signal to decompose it into sub-signals. The output of ridgelet transform is considered as input of new feature selection to identify the best candidates to be used as the forecast engine input. Finally, a new hybrid closed loop forecast engine is proposed based on a neural network and an intelligent algorithm to predict the wind signal. The effectiveness of the proposed forecast model is extensively evaluated on a real-world electricity market through a comparison with well-known forecasting methods. The obtained numerical results demonstrate the validity of proposed method.     

Evaluating the criteria for human resource for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach

This study intends to use a combination of fuzzy Analytic Hierarchy Process (AHP) and fuzzy Decision-making Trial and Evaluation Laboratory (DEMATEL) method in human resource for science and technology (HRST). Specifically, this study first uses AHP to evaluate the weighting for each criterion and then use DEMATEL method to establish contextual relationships among those criteria. We find out Infrastructure might be more critical since it is a cause and will directly influence human resource for science and technology performance. For human resource for science and technology (HRST), improving Infrastructure might be a better choice for the long period of time. Moreover, Education, R&D Expenses and Immediate output are more important second-tier criteria than Value, Cooperation, Labor Market, Human Capital and Intermediate output. Therefore, the improvement should be started with Infrastructure, particularly on identification of the Education, R&D Expenses and Immediate output.     

Prediction of protein cellular localization sites using a hybrid method based on artificial immune system and fuzzy k-NN algorithm

The use of artificial intelligence methods in biological data analysis has been increased recent since performance of the classification and detection systems have improved considerably to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system (AIS) based fuzzy k-NN algorithm with and without cross validation in a class of imbalanced problems in bioinformatics. Furthermore, we devise an unsupervised AIS algorithm in a supervised manner which contains a training stage for data reduction and a classification stage using fuzzy k-NN algorithm. The experiments show the efficacy of the proposed method with promising results. Using the Escherichia coli and yeast database, we compare the classification accuracy of the proposed method with those of other methods which have been proposed in the literature. The proposed hybrid system produced much more accurate results than the Horton and Nakai's method [P. Horton, K. Nakai, Better prediction of protein cellular localization sites with the k-nearest neighbors classifier, in: Proceedings of Intelligent Systems in Molecular Biology, Halkidiki, Greece, 1997, pp. 368–383]. Besides the improvement on the classification accuracy, one of the important aspects of the proposed method is the complexity. As the proposed AIS method incorporates data reduction in the training stage, the training complexity is considerably low comparing with the k-NN classifier.     

Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)

In this study, diagnosis of hepatitis disease, which is a very common and important disease, is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM) and simulated annealing (SA). Simulated annealing is a stochastic method currently in wide use for difficult optimization problems. Intensively explored support vector machine due to its several unique advantages is successfully verified as a predicting method in recent years. We take the dataset used in our study from the UCI machine learning database. The classification accuracy is obtained via 10-fold cross validation. The obtained classification accuracy of our method is 96.25% and it is very promising with regard to the other classification methods in the literature for this problem.     

A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease

Hepatitis is a disease which is seen at all levels of age. Hepatitis disease solely does not have a lethal effect, but the early diagnosis and treatment of hepatitis is crucial as it triggers other diseases. In this study, a new hybrid medical decision support system based on rough set (RS) and extreme learning machine (ELM) has been proposed for the diagnosis of hepatitis disease. RS-ELM consists of two stages. In the first one, redundant features have been removed from the data set through RS approach. In the second one, classification process has been implemented through ELM by using remaining features. Hepatitis data set, taken from UCI machine learning repository has been used to test the proposed hybrid model. A major part of the data set (48.3%) includes missing values. As removal of missing values from the data set leads to data loss, feature selection has been done in the first stage without deleting missing values. In the second stage, the classification process has been performed through ELM after the removal of missing values from sub-featured data sets that were reduced in different dimensions. The results showed that the highest 100.00% classification accuracy has been achieved through RS-ELM and it has been observed that RS-ELM model has been considerably successful compared to the other methods in the literature. Furthermore in this study, the most significant features have been determined for the diagnosis of the hepatitis. It is considered that proposed method is to be useful in similar medical applications.     

Facing the classification of binary problems with a hybrid system based on quantum-inspired binary gravitational search algorithm and K-NN method

Since given classification data often contains redundant, useless or misleading features, feature selection is an important pre-processing step for solving classification problems. This problem is often solved by applying evolutionary algorithms to decrease the dimensional number of features involved. Removing irrelevant features in the feature space and identifying relevant features correctly is the primary objective, which can increase classification accuracy. In this paper, a novel QBGSA–K-NN hybrid system which hybridizes the quantum-inspired binary gravitational search algorithm (QBGSA) with the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) is proposed. The main aim of this system is to improve classification accuracy with an appropriate feature subset in binary problems. We evaluate the proposed hybrid system on several UCI machine learning benchmark examples. The experimental results show that the proposed method is able to select the discriminating input features correctly and achieve high classification accuracy which is comparable to or better than well-known similar classifier systems.     

A new family of metrics for compact, convex (fuzzy) sets based on a generalized concept of mid and spread

One of the most important aspects of the (statistical) analysis of imprecise data is the usage of a suitable distance on the family of all compact, convex fuzzy sets, which is not too hard to calculate and which reflects the intuitive meaning of fuzzy sets. On the basis of expressing the metric of Bertoluzza et al. [C. Bertoluzza, N. Corral, A. Salas, On a new class of distances between fuzzy numbers, Mathware Soft Comput. 2 (1995) 71-84] in terms of the mid points and spreads of the corresponding intervals we construct new families of metrics on the family of all d-dimensional compact convex sets as well as on the family of all d-dimensional compact convex fuzzy sets. It is shown that these metrics not only fulfill many good properties, but also that they are easy to calculate and easy to manage for statistical purposes, and therefore, useful from the practical point of view.     

Proposing a new integrated model based on sustainability balanced scorecard (SBSC) and MCDM approaches by using linguistic variables for the performance evaluation of oil producing companies

Using the balanced scorecard approach based on sustainable development parameters is a powerful and useful methodology to evaluate the sustainable performance of organization or company. In this paper, a new approach based on sustainability balanced scorecard (SBSC) and multi criteria decision making (MCDM) approaches is developed for evaluating the performance of oil producing companies in Iran. For reflecting the interdependent relationships among factors influencing the problem under consideration, analytical network process (ANP), a branch of the MCDM techniques, is employed. However, using the ANP method for calculating the preference ratings of alternatives is a time-consuming and bothersome process; therefore, COPRAS (COmplex PRoportional ASsessment) technique is adopted to prioritize the feasible alternatives in terms of linguistic variables. Based on this study, the results demonstrate the effectiveness of the proposed model. The performance evaluation model proposed by using a combination of the MCDM methods and the SBSC approach helps authorities to make an attempt for achieving a competitive advantage.