Real-valued Negative Selection Research Articles

A hybrid real-valued negative selection algorithm with variable-sized detectors and the k-nearest neighbors algorithm

A negative selection algorithm generates detectors to realize abnormality detection by simulating the maturation process of T cells in human immunity. Holes are areas of feature space that cannot be covered by the detector set and are the major factor in the degradation of algorithm performance. Conventional methods alleviate the hole problem by minimizing the coverage area of the holes. In this study, we approach the issue from a different angle. Holes are prone to form in the boundary area between the self and nonself regions, and when the self and the nonself cross or overlap, the hole problem becomes more serious. The k-nearest neighbors (k-NN) algorithm is more suitable than other methods for pending instance sets where the class domain crosses or overlaps more. Therefore, we propose a hybrid real-valued negative selection algorithm with variable-sized detectors (V-Detector) and the k-NN algorithm, abbreviated as V-Detector-kNN. The V-Detector-kNN hybrid algorithm first uses the V-Detector algorithm to classify, and then, for the problem that the nonself instances in the holes are misclassified as selfs, k-NN is introduced to classify those misclassified instances to improve the detection rate. Theoretical analysis proves that the V-Detector-kNN algorithm that we proposed has a higher detection rate than the V-Detector algorithm in most cases. Comparative experiments with 5 different algorithms on 9 UCI datasets show that our proposed algorithm ranks first in detection rate.

Pengenalan Pola Angka Menggunakan Pendekatan Optimisasi Sistem Kekebalan Buatan (Artificial Immune System)

The general election in Indonesia itself still experiences technical and non-technical problems where the technical problems occur in the recapitulation of votes from sheet C1 which are still incorrectly inputted and done manually. The problem occurred with the difference in the uploaded C1 data and the data in the KPU Situng and the C1 sheet uploaded was blurry, unclear, sheet C1 which was crossed out or folded in the KPU Situng. The purpose of this research is to reduce errors in data input and change the work that is done manually to the system, create a number pattern recognition system using an Artificial Immune System optimization approach, test and analyze the work of the system by taking into account the level of accuracy, preciseness and speed in recognize number patterns. The system created to applies an artificial immune system optimization approach with the Artificial Immune System using the Randomized Real-Valued Negative Selection Algorithm algorithm.

HD-NSA: A real-valued negative selection algorithm based on hierarchy division

The negative selection algorithm (NSA) is an important algorithm for generating immune detectors in artificial immune systems. However, the original NSA randomly generates candidate detectors that produce a large number of redundant detectors, and it is difficult to cover the entire antibody space. Moreover, the randomly generated candidate detectors have to be compared with all the self-sets; therefore, the inefficient generation of the detector seriously influences the application of NSA. To overcome these defects, a real-valued NSA based on hierarchy division (HD-NSA) is proposed. First, the feature space is divided into self and non-self subgrids, and the center point of the non-self subgrid is specified as the candidate detector, and the specified candidate detector is compared with the self-antigens located in adjacent subgrids rather than with all the self-sets. Theoretical analysis demonstrated that the HD-NSA can effectively reduce the time complexity of the NSA algorithm. Furthermore, experiments on the Abalone data set show that the detector training time of HD-NSA decreased by 97.9%, 71.2%, 56.9% and 90.1%, respectively, compared with the classical RNSA, V-Detector, GF-RNSA and BIORV-NSA, whereas the detector detection rate increased by 50%, 25.8%, 13.8% and 10.5%, respectively.

Using known nonself samples to improve negative selection algorithm

Negative selection algorithm is the core algorithm of artificial immune system. It only uses the self for training and generates detectors to detect abnormalities. Holes are feature space areas that the detector fails to cover, it is the root cause of the performance degradation of the negative selection algorithm. The conventional method generates a large number of detectors randomly to repair the holes, which is time-consuming and not effective. To alleviate the problem, we propose a V-Detector-KN algorithm in this paper. V-Detector is the abbreviation of the real-valued negative selection algorithm with Variable-sized Detectors, KN represents Known Nonself. The V-Detector-KN algorithm uses the known nonself as the candidate detector to further generate the detector based on the V-Detector randomly generated detector, so as to realize the repair of holes. Compared with the conventional method to randomly generate detectors to repair holes, our proposed V-Detector-KN method uses known nonself to repair holes, reducing the randomness and blindness of hole repair. Theoretical analysis shows that the detection rate of our algorithm is not lower than that of the conventional V-Detector algorithm. The results of experiment comparing with other 6 algorithms on 7 UCI data sets show the superiority of our proposed algorithm.

An improved real-valued negative selection algorithm based on the constant detector for anomaly detection

Compared with the traditional negative selection algorithms produce detectors randomly in whole state space, the boundary-fixed negative selection algorithm (FB-NSA) non-randomly produces a layer of detectors closely surrounding the self space. However, the false alarm rate of FB-NSA is higher than many anomaly detection methods. Its detection rate is very low when normal data close to the boundary of state space. This paper proposed an improved FB-NSA (IFB-NSA) to solve these problems. IFB-NSA enlarges the state space and adds auxiliary detectors in appropriate places to improve the detection rate, and uses variable-sized training samples to reduce the false alarm rate. We present experiments on synthetic datasets and the UCI Iris dataset to demonstrate the effectiveness of this approach. The results show that IFB-NSA outperforms FB-NSA and the other anomaly detection methods in most of the cases.

A modified real-value negative selection detector-based oversampling approach for multiclass imbalance problems

A skewed distribution poses a major challenge in multiclass imbalanced problems and has attracted growing interest in the engineering and research communities. Conventional classifiers pose limitations in handling multiclass imbalanced data sets since they were originally designed to handle a balanced distribution. This paper proposes a modified real-value negative selection detector-based oversampling approach for the multiclass imbalance problem. Different from previous works, we have modified and introduced the traditional real-value negative selection algorithm to address the issue of the multiclass imbalance problem. First, the modified real-value negative selection technique is used to create detectors for each minority class. Then, a supervision mechanism is designed using these detectors to prevent overgeneralization. Furthermore, the method of selection weight based on crowding density and detectors is devised with the aim of reducing the within-class imbalance for each minority class. Finally, simulations were conducted on the public real-world multiclass imbalanced data sets from Knowledge Extraction based on Evolutionary Learning (KEEL) and the University of California Irvine (UCI). The experimental and statistical results have demonstrated that the proposed algorithm obtains better performance compared with seven well-known oversampling algorithms in terms of five metrics (precision, recall, F-measure, Multiclass G-mean (MG), and Multiclass Area Under the ROC (MAUC)) and four types of classifiers.

Multidomain Features-Based GA Optimized Artificial Immune System for Bearing Fault Detection

This paper proposes a novel multidomain features-based genetic algorithm (GA) optimized artificial immune system (AIS) framework for fault detection in real systems. Different from native real-valued negative selection algorithm (RNSA) that operates in original data space, this algorithm utilizes feature space transformation and diversity factor-based GA for optimized detector distribution in nonself feature space. The proposed framework comprises three stages namely; feature extraction, unsupervised feature selection, and GA optimized AIS. In the first stage, signal processing methods are applied to extract multidomain features (time-domain statistical, frequency domain statistical, and special features) of the system. In the second stage, two unsupervised methods namely, ${k}$ -NN clustering and pretraining using deep learning neural network are proposed for dominant fault-characterizing feature selection. Finally, in the third stage, the fault-characterizing feature vectors are used for system status categorization (i.e., normal, fault) using selected (fault-characterizing) features-based AIS method. The efficacy of the proposed framework is verified through experiments on motor bearing fault detection using vibration signal. The major accomplishment of the proposed combination of space transformation, feature selection and AIS (anomaly classification) techniques is the alleviation of computational burden on RNSA implementation. Moreover, GA optimized AIS fault diagnosis based on well-established features gives improved detection performance.

Predicting Crude Oil Price Using Fuzzy Rough Set and Bio-Inspired Negative Selection Algorithm

The need to accurately predict and make right decisions regarding crude oil price motivates the proposition of an alternative algorithmic method based on real-valued negative selection with variable-sized detectors (V-Detectors), by incorporating with fuzzy-rough set feature selection (FRFS) for predicting the most appropriate choices. The objective of this study is enhancing the performance of V-Detectors using FRFS for prices of crude oil. Applying FRFS serves to prune the number of features by retaining the most informative and critical features. The V-Detectors then trains and tests the features. Different radius values are applied for V-Detectors. Experimental outcome in comparison with established algorithms such as support vector machine, naïve bayes, multi-layer perceptron, J48, non-nested generalized exemplars, IBk, fuzzy-roughNN, and vaguely quantified nearest neighbor demonstrates that FRFS-V-Detectors is proficient and valuable for insightful knowledge on crude oil price. Thus, it can assist in establishing oil price market policies on the international scale.

Real-value negative selection over-sampling for imbalanced data set learning

The learning problem from imbalanced data set poses a major challenge in data mining community. Conventional machine learning algorithms show poor performance in dealing with the classification problems of imbalanced data set since they are originally designed to work with balanced class distribution. In this paper, we propose a new over-sampling technique, which uses the real-value negative selection (RNS) procedure to generate artificial minority data with no requirement of actual minority data available. The generated minority data with rare actual minority data if available are combined with the majority data as input to a bi-class classification approach for learning. In the experiments, we demonstrate the effectiveness of RNS in avoiding the problems often encountered by the existing over-sampling methods such as the generation of noisy instances and almost duplicated instances in the same clusters. Moreover, the extensive experimental results on the different imbalanced datasets from UCI repository and real-world imbalanced datasets show that when dealing with the classification of imbalanced datasets, the proposed hybrid approach can achieve better performance in terms of both G-Mean and F-Measure evaluation metrics as compared to the other existing imbalanced dataset classification techniques.

A Clone Selection Based Real-Valued Negative Selection Algorithm

Excessive detectors, high time complexity, and loopholes are main problems which current negative selection algorithms have face and greatly limit the practical applications of negative selection algorithms. This paper proposes a real-valued negative selection algorithm based on clonal selection. Firstly, the algorithm analyzes the space distribution of the self set and gets the set of outlier selves and several classification clusters. Then, the algorithm considers centers of clusters as antigens, randomly generates initial immune cell population in the qualified range, and executes the clonal selection algorithm. Afterwards, the algorithm changes the limited range to continue the iteration until the non-self space coverage rate meets expectations. After the algorithm terminates, mature detector set and boundary self set are obtained. The main contributions lie in (1) introducing the clonal selection algorithm and randomly generating candidate detectors within the stratified limited ranges based on clustering centers of self set; generating big-radius candidate detectors first and making them cover space far from selves, which reduces the number of detectors; then generating small-radius candidate detectors and making them gradually cover boundary space between selves and non-selves, which reduces the number of holes; (2) distinguishing selves and dividing them into outlier selves, boundary selves, and internal selves, which can adapt to the interference of noise data from selves; (3) for anomaly detection, using mature detector set and boundary self set to test at the same time, which can effectively improve the detection rate and reduce the false alarm rate. Theoretical analysis and experimental results show that the algorithm has better time efficiency and detector generation quality according to classic negative selection algorithms.

Layered and Real-Valued Negative Selection Algorithm for Fault Detection

In this paper, the challenging task of the development of a generic fault detection (FD) method is addressed. While the past FD research has primarily focused on modeling and signal-processing methods that are problem specific and require complete knowledge of the system model and fault types, this paper presents a novel layered and real-valued negative-selection algorithm (LRNSA)-based FD method independent of prior knowledge of fault types and patterns. Specifically, in the training phase, the nonself-space is divided into different layers for effective generation and distribution of detectors using normal (self) data. The major accomplishments of the proposed method are improved nonself-space coverage of the uncovered gaps (holes), followed by the formation of cluster detector with large radius. To test the capabilities of the developed method, the generated specialized detector distribution is studied using bearing fault modeling in a three-phase induction motor. The proposed method is subsequently investigated and validated by applying it to an actual induction motor for different types of bearing faults. Finally, the comparative results on the benchmark dataset demonstrate the superiority of the proposed method compared to the state-of-the-art machine learning algorithms in terms of higher FD accuracy and quick detection with reduced online detection time.

An antigen space density based real-value negative selection algorithm

The negative selection algorithm (NSA) is an important detector generation algorithm for artificial immune systems. In high-dimensional space, antigens (data samples) distribute sparsely and unevenly, and most of them reside in low-dimensional subspaces. Therefore, traditional NSAs, which randomly generate detectors without considering the distribution of the antigens, cannot effectively distinguish them. To overcome this limitation, the antigen space density based real-value NSA (ASD-RNSA) is proposed in this paper. The ASD-RNSA contains two new processes. First, in order to improve detection efficiency, ASD-RNSA utilizes the antigen space density to calculate the low-dimensional subspaces where antigens are densely gathered and directly generate detectors in these subspaces. Second, to eliminate redundant detectors and prevent the algorithm from prematurely converging in high-dimensional space, ASD-RNSA suppresses candidate detectors that are recognized by other mature detectors and adopts an “antibody suppression rate” to replace the “expected coverage” as the termination condition. Experimental results show that ASD-RNSA achieves a better detection rate and has better generation quality than classical real-value NSAs.

Technique for Intrusion Detection based on Cutting-based Real-valued Negative Selection

Technique for Intrusion Detection based on Cutting-based Real-valued Negative Selection

An immune optimization based real-valued negative selection algorithm

Negative selection algorithms are important for artificial immune systems to produce detectors. But there are problems such as high time complexity, large number of detectors, a lot of redundant co...

EvoSeedRNSAII: An improved evolutionary algorithm for generating detectors in the real-valued Negative Selection Algorithms

In the previous work, a detector generation algorithm, named as the EvoSeedRNSA, is proposed. A genetic algorithm is adopted in the EvoSeedRNSA to evolve the random seeds to generate an approximately optimal detector set. This paper proposes an improved EvoSeedRNSA, named as the EvoSeedRNSAII, to generate a more efficient detector set. A multi-group random seed encoding scheme is designed to represent the individuals and different detector generation sequences are discussed. The experiments demonstrate that the EvoSeedRNSAII has a better performance than the EvoSeedRNSA.

An Immune Based Patient Anomaly Detection using RFID Technology

Detecting of anomalies patients data are important to gives early alert to hospital, in this paper will explore on anomalies patient data detecting and processing using artificial computer intelligent system. Artificial Immune System (AIS) is an intelligent computational technique refers to human immunology system and has been used in many areas such as computer system, pattern recognition, stock market trading, etc. In this case, real value negative selection algorithm (RNSA) of artificial immune system used for detecting anomalies patient body parameters such as temperature. Patient data from monitoring system or database classified into real valued, real negative selection algorithm results is real values deduction by RNSA distance, the algorithm used is minimum distance and the value of detector generated for the algorithm. The real valued compared with the distance of data, if the distance is less than a RNSA detector distance then data classified into abnormal. To develop real time detecting and monitoring system, Radio Frequency Identification (RFID) technology has been used in this system. Keywords: AIS, RNSA, RFID, AbnormalDOI:Â 10.18495/comengapp.21.121142

Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis

This hybrid methodology for structural health monitoring (SHM) is based on immune algorithms (IAs) and symbolic time series analysis (STSA). Real-valued negative selection (RNS) is used to detect damage detection and adaptive immune clonal selection algorithm (AICSA) is used to localize and quantify the damage. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. This paper explains the mathematical basis of STSA and the procedure of the hybrid methodology. It also describes the results of an simulation experiment on a five-story shear frame structure that indicated the hybrid strategy can efficiently and precisely detect, localize and quantify damage to civil engineering structures in the presence of measurement noise.

Study on Immune Relevant Vector Machine Based Intelligent Fault Detection and Diagnosis Algorithm

An immune relevant vector machine (IRVM) based intelligent classification method is proposed by combining the random real-valued negative selection (RRNS) algorithm and the relevant vector machine (RVM) algorithm. The method proposed is aimed to handle the training problem of missing or incomplete fault sampling data and is inspired by the “self/nonself” recognition principle in the artificial immune systems. The detectors, generated by the RRNS, are treated as the “nonself” training samples and used to train the RVM model together with the “self” training samples. After the training succeeds, the “nonself” detection model, which requires only the “self” training samples, is obtained for the fault detection and diagnosis. It provides a general way solving the problems of this type and can be applied for both fault detection and fault diagnosis. The standard Fisher's Iris flower dataset is used to experimentally testify the proposed method, and the results are compared with those from the support vector data description (SVDD) method. Experimental results have shown the validity and practicability of the proposed method.

A Real-Valued Negative Selection Algorithm Based on Grid for Anomaly Detection

Negative selection algorithm is one of the main algorithms of artificial immune systems. However, candidate detectors randomly generated by traditional negative selection algorithms need to conduct self-tolerance with all selves in the training set in order to eliminate the immunological reaction. The matching process is the main time cost, which results in low generation efficiencies of detectors and application limitations of immune algorithms. A novel algorithm is proposed, named GB-RNSA. The algorithm analyzes distributions of the self set in real space and regards then-dimensional [0, 1] space as the biggest grid. Then the biggest grid is divided into a finite number of sub grids, and selves are filled in the corresponding subgrids at the meantime. The randomly generated candidate detector only needs to match selves who are in the grid where the detector is and in its neighbor grids, instead of all selves, which reduces the time cost of distance calculations. And before adding the candidate detector into mature detector set, certain methods are adopted to reduce duplication coverage between detectors, which achieves fewer detectors covering the nonself space as much as possible. Theory analysis and experimental results demonstrate that GB-RNSA lowers the number of detectors, time complexity, and false alarm rate.

Fault Diagnosis of Induction Motor Based on Wavelet Packet Analysis and Negative Selection Algorithm

For the spectrum characteristics of the motor vibration, the vibration fault diagnosis method of motor rotor rubbing based on wavelet packet transform and real-valued negative selection algorithm is put forward. Using wavelet packet analysis to energy analysis of rotor rubbing and extracting the fault feature vectors, then by real-valued negative selection algorithm to identify the normal and failure mode eigenvectors. The experimental results show that with this method all the rotor rubbing faults can be detected comprehensive and rapidly. This method has high feasibility of the wavelet packet analysis and real-valued negative selection algorithm in the rotor rubbing fault diagnosis.