Artificial Immune Recognition System Research Articles

Prediction of faults-slip-through in large software projects: an empirical evaluation

A large percentage of the cost of rework can be avoided by finding more faults earlier in a software test process. Therefore, determination of which software test phases to focus improvement work on has considerable industrial interest. We evaluate a number of prediction techniques for predicting the number of faults slipping through to unit, function, integration, and system test phases of a large industrial project. The objective is to quantify improvement potential in different test phases by striving toward finding the faults in the right phase. The results show that a range of techniques are found to be useful in predicting the number of faults slipping through to the four test phases; however, the group of search-based techniques (genetic programming, gene expression programming, artificial immune recognition system, and particle swarm optimization---based artificial neural network) consistently give better predictions, having a representation at all of the test phases. Human predictions are consistently better at two of the four test phases. We conclude that the human predictions regarding the number of faults slipping through to various test phases can be well supported by the use of search-based techniques. A combination of human and an automated search mechanism (such as any of the search-based techniques) has the potential to provide improved prediction results.

A Radial Basis Function based Artificial Immune Recognition System for Classification

A Radial Basis Function based Artificial Immune Recognition System for Classification

Using Artificial Immune Recognition Systems in Order to Detect Early Breast Cancer

In this work, a decision support system for early breast cancer detection is presented. In hard to diagnose cases, different examinations (i.e. mammography, ultrasonography and magnetic resonance imaging) provide contradictory findings and patient is guided to biopsy for definite results. The proposed method employs a Correlation Feature Selection procedure and an Artificial Immune Recognition System (AIRS) and is evaluated using real data collected from 53 subjects with contradictory diagnoses. Comparative results with commonly used artificial intelligence classifiers verify the suitability of the AIRS classifier. The application of such an approach can reduce the number of unnecessary biopsies.

A neuro-fuzzy immune inspired classifier for task-oriented texts

Increasing growth of task-oriented texts specifically in organizations, have become a catastrophe nowadays. To overcome this problem, potential classification methods are improved. This paper outlines the capability of neuro-fuzzy approach and artificial immune recognition systems to enhance task-oriented texts classification. Task-oriented texts stand for various kinds of texts which are organized to help the users with their different tasks such as: research, development, learning, justification, innovation and analysis. In this respect, seven major attributes with three nominal values of low, medium and high are considered to classify text into six task classes. To illustrate the capabilities of proposed approaches, Takagi-Sugeno as a neuro-fuzzy approach using lolimot learning algorithm, is compared with multilayer perceptron MLP, and Radial Basis Function RBF. In the meantime, various versions of Artificial Immune Recognition Systems AIRS including AIRS1, AIRS2, Parallel AIRS and Modified AIRS with Fuzzy K-Nearest neighbor Fuzzy-KNN are also evaluated in comparison with the above mentioned algorithms to classify the same text. The experimental results of classification on a dataset of 540 data reveals that, due to the distributed characteristics of a text, and the complexity of tasks respectively, evolutionary and neuro-fuzzy methods are expected to be particularly workable and successfully applicable to task-oriented text classification specifically for the purpose of decision support.

Re-visiting the artificial immune recognition system: a survey and an improved version

This paper surveys the major works related to an artificial immune system based classifier that was proposed in the 2000s, namely, the artificial immune recognition system (AIRS) algorithm. This survey has revealed that most works on AIRS was dedicated to the application of the algorithm to real-world problems rather than to theoretical developments of the algorithm. Based on this finding, we propose an improved version of the AIRS algorithm which we dub AIRS3. AIRS3 takes into account an important parameter that was ignored by the original algorithm, namely, the number of training antigens represented by each memory cell at the end of learning (numRepAg). Experiments of the new AIRS3 algorithm on data sets taken from the UCI machine learning repository have shown that taking into account the numRepAg information enhances the classification accuracy of AIRS.

Alert correlation using artificial immune recognition system

High volumes of low-level alerts that are generated by intrusion detection systems (IDSs) are serious obstacle for using them effectively. These high volumes of alerts overwhelm system administrators in such a way that they cannot manage and interpret them. Alert correlation is used to reduce the number of alerts and increase their level of abstraction. It selects a group of low-level alerts and converts them into a higher level attack and then produces a high-level alert for them. In this paper, a new artificial immune system-based alert correlation system is presented, named AISAC. It learns the correlation probability between each pair of alert types and uses this knowledge to extract the attack scenarios. AISAC does not need intensive domain knowledge and rule definition efforts. It also does not need to manually update the extracted knowledge. The computational cost of learning algorithm is linear, and the initial learning is done by a very limited general data in offline mode. AISAC is evaluated by DARPA 2000 and netForensics Honeynet data. Results show that although it uses a relatively simple algorithm, it generates the attack graphs with acceptable accuracy.

Diagnosis of Diabetes Diseases Using an Artificial Immune Recognition System2 (AIRS2) with Fuzzy K-nearest Neighbor

The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disease dataset used in our work is retrieved from UCI machine learning repository. The performances of the AIRS2 and MAIRS2 are evaluated regarding classification accuracy, sensitivity and specificity values. The highest classification accuracy obtained when applying the AIRS2 and MAIRS2 using 10-fold cross-validation was, respectively 82.69% and 89.10%.

Corrigendum to “Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing” [Pattern Recognition 39(11) (2006) 2186–2193

Corrigendum to “Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing” [Pattern Recognition 39(11) (2006) 2186–2193

A new approach of obtaining reservoir operation rules: Artificial immune recognition system

Artificial immune recognition system (AIRS) was employed in this paper as a new approach of data mining to extract operating rules on a case of water-supply reservoir, and the comparisons were performed between the operating rules obtained by the system and those by RBF. Further statistics about distance distributions between the acquired operating rules and training or testing samples are made to indirectly illuminate the impacts on the performances or behaviors of AIRS from three aspects of different affinity functions, training (testing) sample spatial distribution and supplementary samples in high nonlinear space of operation decision. The results indicate that AIRS can effectively extract water-supply operating rules and enrich the reservoir operation researches.

An Efficient and Effective Immune Based Classifier

Problem statement: Artificial Immune Recognition System (AIRS) is most popular and effective immune inspired classifier. Resource competition is one stage of AIRS. Resource competition is done based on the number of allocated resources. AIRS uses a linear method to allocate resources. The linear resource allocation increases the training time of classifier. Approach: In this study, a new nonlinear resource allocation method is proposed to make AIRS more efficient. New algorithm, AIRS with proposed nonlinear method, is tested on benchmark datasets from UCI machine learning repository. Results: Based on the results of experiments, using proposed nonlinear resource allocation method decreases the training time and number of memory cells and doesn't reduce the accuracy of AIRS. Conclusion: The proposed classifier is an efficient and effective classifier.

Decision support system using artificial immune recognition system for fault classification of centrifugal pump

Centrifugal pumps are a crucial part of many industrial plants. Early detection of faults in pumps can increase their reliability, reduce energy consumption, service and maintenance costs, and increase their life-cycle and safety, thus resulting in a significant reduction in life-time costs. Vibration analysis is a very popular tool for condition monitoring of machinery like pumps, turbines and compressors. The proposed method is based on a novel immune inspired supervised learning algorithm which is known as artificial immune recognition system (AIRS). This paper compares the fault classification efficiency of AIRS with hybrid systems such as principle component analysis (PCA)-Naive Bayes and PCA-Bayes Net. The robustness of the proposed method is examined using its classification accuracy and kappa statistics. It is observed that the AIRS-based system outperforms the other two methods considered in the present study.

A color image watermarking scheme based on artificial immune recognition system

This study suggests a novel watermarking technique that uses artificial immune recognition system to protect color image’s intellectual property rights. The watermark is embedded in the blue channel of a color image. m-bit binary sequence embedded into the color image is used to train artificial immune recognition system. With this composed technique, extracting the watermark which is embedded into the color image is carried out using artificial immune recognition system. It is observed that the composed technique achieves high performance to process of extracting this watermark. The watermark is extracted successfully from the watermarked image after various image processing attacks as well.

Strategy for genotoxic impurities: Hazard identification, risk assessment and management in Angelini

This paper presents a novel method for feature selection by proposing an improved artificial immune recognition system (IAIRS) using the opposite sign test (OST). We use the nearest neighbor algorithm as the classifier. Forty-four data sets from the UCI and KEEL repository and from eight benchmark gene expression micro-array data sets were collected for evaluation purposes. This evaluation measures the effectiveness of the proposed approach. To investigate the capability of IAIRS, we compared our result with several features selection methods and classifier based methods. Moreover, we compared our results with the results obtained by several well-known algorithms from the previous literature. The performance measures were based on accuracy and the Cohen Kappa. A non-parametric statistical test was used to justify the performance of our proposed method. We confirmed that IAIRS is significantly better than other methods.

Unlabelled extra data do not always mean extra performance for semi‐supervised fault prediction

Abstract: This research focused on investigating and benchmarking several high performance classifiers called J48, random forests, naive Bayes, KStar and artificial immune recognition systems for software fault prediction with limited fault data. We also studied a recent semi‐supervised classification algorithm called YATSI (Yet Another Two Stage Idea) and each classifier has been used in the first stage of YATSI. YATSI is a meta algorithm which allows different classifiers to be applied in the first stage. Furthermore, we proposed a semi‐supervised classification algorithm which applies the artificial immune systems paradigm. Experimental results showed that YATSI does not always improve the performance of naive Bayes when unlabelled data are used together with labelled data. According to experiments we performed, the naive Bayes algorithm is the best choice to build a semi‐supervised fault prediction model for small data sets and YATSI may improve the performance of naive Bayes for large data sets. In addition, the YATSI algorithm improved the performance of all the classifiers except naive Bayes on all the data sets.

An Application of Artificial Immune Recognition System for Prediction of Diabetes Following Gestational Diabetes

Diabetes mellitus (DM) is a disease prevalent in population and is not easily perceived in its initial stage but may sway a patient very seriously in later stage. In accordance with the estimation of World Health Organization (WHO), there will be 370 million diabetics which are 5.4% of the global people in 2030, so it becomes more and more important to predict whether a pregnant woman has or is likely to acquire diabetes. This study is conducted with the use of the machine learning-Artificial Immune Recognition System (AIRS)-to assist doctors in predicting pregnant women who have premonition of type 2 diabetes. AIRS is proposed by Andrew Watkins in 2001 and it makes use of the metaphor of the vertebrate immune system to recognize antigens, select clone, and memorize cells. Additionally, AIRS includes a mechanism, limited resource, to restrain the number of memory cells from increasing uncontrollably. It has also showed positive results on problems in which it was applied. The objective of this study is to investigate the feasibility in using AIRS to predict gestational diabetes mellitus (GDM) subsequent DM. The dataset of diabetes has imbalanced data, but the overall classification recall could still reach 62.8%, which is better than the traditional method, logistic regression, and the technique which is thought as one of the powerful classification approaches, support vector machines (SVM).

Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases

Abstract: The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS‐SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10‐fold cross‐validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS‐SVM classifier and the AIRS classifier respectively using 10‐fold cross‐validation. It is shown that the LS‐SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.

Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem

Software quality engineering comprises of several quality assurance activities such as testing, formal verification, inspection, fault tolerance, and software fault prediction. Until now, many researchers developed and validated several fault prediction models by using machine learning and statistical techniques. There have been used different kinds of software metrics and diverse feature reduction techniques in order to improve the models’ performance. However, these studies did not investigate the effect of dataset size, metrics set, and feature selection techniques for software fault prediction. This study is focused on the high-performance fault predictors based on machine learning such as Random Forests and the algorithms based on a new computational intelligence approach called Artificial Immune Systems. We used public NASA datasets from the PROMISE repository to make our predictive models repeatable, refutable, and verifiable. The research questions were based on the effects of dataset size, metrics set, and feature selection techniques. In order to answer these questions, there were defined seven test groups. Additionally, nine classifiers were examined for each of the five public NASA datasets. According to this study, Random Forests provides the best prediction performance for large datasets and Naive Bayes is the best prediction algorithm for small datasets in terms of the Area Under Receiver Operating Characteristics Curve (AUC) evaluation parameter. The parallel implementation of Artificial Immune Recognition Systems (AIRS2Parallel) algorithm is the best Artificial Immune Systems paradigm-based algorithm when the method-level metrics are used.

A New Approach to Diagnosing of Importance Degree of Obstructive Sleep Apnea Syndrome: Pairwise AIRS and Fuzzy-AIRS Classifiers

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).

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.

A negative selection algorithm for classification and reduction of the noise effect

Artificial Immune Systems (AIS) are a type of intelligent algorithm inspired by the principles and processes of the human immune system. In the last decade, applications of AIS have been studied in various fields. In the application of change/anomaly detection, negative selection algorithms of AIS have been successfully applied. However, negative selection algorithms are not appropriate for multi-class classification problems, because they do not have a mechanism to minimize the danger of overfitting and oversearching. In this paper, we propose a new algorithm to overcome this drawback and to extend the application area of negative selection algorithms to multi-class classification. The algorithm we propose is named Artificial Negative Selection Classifier (ANSC). We investigate the tolerance of ANSC against noise, and introduce a method to reduce the effect of noise into ANSC. The accuracy and data reduction are compared with those from the Artificial Immune Recognition System (AIRS), which is a well known and effective classifier of AIS. The results show that our algorithm is useful for classification problems and the reduction of the noise effect.