A Supervised Classifier Based on Artificial Immune System

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

Effective distribution outage cause identification can help expedite the restoration procedure and improve the system availability. The fuzzy classification E-algorithm and the immune system inspired classification algorithm, artificial immune recognition system (AIRS), have demonstrated good capabilities in outage cause identification, especially with the existence of imbalanced data. E-algorithm extracts inference rules but is computational demanding; AIRS has the quick searching capability but is lack of rule extraction capability. In this paper, fuzzy artificial immune recognition system (FAIRS) has been proposed to take advantage of the strengths of E-algorithm and AIRS. FAIRS is applied to Duke Energy outage data for cause identification using three major customer interruption causes (tree, animal, and lightning) as prototypes; and FAIRS achieves comparable fault diagnosis performance with two base algorithms while being able to extract linguistic rules to explain the inference within significantly reduced computing time than E-algorithm.

Data reduction is an indispensable part of pattern classification processes in many cases. If the number of samples is excessive, sample reduction or data reduction algorithms can be used for an effective processing time and reliable successive results. Many methods have been used for data reduction. Fuzzy c-means is one of these methods and it is widely used in such applications as clustering algorithms. In this study, we applied a different clustering algorithm, an artificial immune system (AIS), for the data reduction process. We realized the performance evaluation experiments on the standard Chainlink and Iris datasets, while the main application was conducted using the Wisconsin Breast Cancer and Pima Indian datasets, which were taken from the University of California, Irvine Machine Learning Repository. For these datasets, the performance of the AIS in the data reduction process was compared with the fuzzy c-means clustering algorithm, in which a multilayer perceptron artificial neural network was used as a classifier after the data reduction processes. The obtained results show that the maximum classification accuracies were obtained as 73.96 % for the Pima Indian Diabetes dataset and 97.80% for the Wisconsin Breast Cancer dataset with the AIS and the compression rates were 80% and 40% for these results. For fuzzy c-means clustering, however, the aforementioned accuracies were obtained as 63% and 86.69% for the Pima Indian Diabetes and Wisconsin Breast Cancer datasets, respectively. Moreover, the compression rates for these results for fuzzy c-means were 90% and 70%. When the mean classification accuracy values over the experimented compression rates were taken into consideration, the AIS reached a mean classification accuracy of 70.07% for the Pima Indian Diabetes dataset, while 47.64% was obtained by fuzzy c-means for this dataset. For the Wisconsin Breast Cancer dataset, however, the mean classification accuracies of the AIS and fuzzy c-means methods were recorded as 94.90% and 75.43%, respectively.

Automated identification of diseases related to lymph system from lymphography data using artificial immune recognition system with fuzzy resource allocation mechanism (fuzzy-AIRS)

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

Artificial Immune Systems, a biologically inspired computing paradigm such as Artificial Neural Networks, Genetic Algorithms, and Swarm Intelligence, embody the principles and advantages of vertebrate immune systems. It has been applied to solve several complex problems in different areas such as data mining, computer security, robotics, aircraft control, scheduling, optimization, and pattern recognition. There is an increasing interest in the use of this paradigm and they are widely used in conjunction with other methods such as Artificial Neural Networks, Swarm Intelligence and Fuzzy Logic. In this chapter, we demonstrate the procedure for applying this paradigm and bio-inspired algorithm for developing software fault prediction models. The fault prediction unit is to identify the modules, which are likely to contain the faults at the next release in a large software system. Software metrics and fault data belonging to a previous software version are used to build the model. Fault-prone modules of the next release are predicted by using this model and current software metrics. From machine learning perspective, this type of modeling approach is called supervised learning. A sample fault dataset is used to show the elaborated approach of working of Artificial Immune Recognition Systems (AIRS).

This paper proposed an improved artificial immune recognition system (IAIRS) based on the average scatter matrix trace (ASMT) criterion. In essence, the artificial immune recognition system (AIRS) is an evolving algorithm. Through clonal expansion, affinity maturation, resource competition and immune memory etc, a set of new samples (memory cells) is produced. The ASMT of memory cells will be decreased and the minimized ASMT can be as the optimal criterion of AIRS. The IAIRS algorithm is demonstrated on a number of benchmark data sets effectively.

Computer science has always mixed the concepts of biology and computers to enhance the way in which systems are designed. Artificial Immune System (AIS) is a Computational Intelligence strategy dependent on an organically enlivened computational system that can be utilized for taking care of complex computational issues. It tends to be seen that AIS is an incredibly various locale of research, going from the modeling immune systems to complex algorithms for specific applications. This paper exhibits an exhaustive survey of different frameworks developed in the artificial immune system and its application. Reviews of frameworks in AIS are uncommon and henceforth this paper gives an inside out audit of progressing research and challenges in AIS. We start by presenting AIS and give a thorough survey of different systems in AIS and its application in anomaly detection. We investigate the utilization of AIS in the Intrusion Detection System named the Cyber Immune System(CIS) and compares various AIS works applied to CIS. We conclude with various future extensions in the area of AIS research.

In engineering application, the characteristics of the control system are entirely determined by the system controller once the controlled object has been chosen. Improving the traditional controller or constructing the new controller is an unfading study field of control theory and application. The control system is greatly enriched and developed by this way. As a complicated self-adaptable system, the biological immune system can effectively and smoothly stand against antigens and viruses intruded into organism. It is possible to improve the self-learning, adaptive and robustness capability of the control system through embedded an artificial immune controller in control system. Based on the biological immune mechanism and artificial immune model, this chapter attempts to study the immune controller design and application in traditional control system..First, a kind of artificial immune controller is proposed based on the T-B cells immunity. The boundedness and the stability of SISO control systems, which constructed by the artificial immune controller, are proved by the little gain theorem. A general controller structure frame based on the T-B cells immunity is proposed, which includes the same kind of controller proposed previously. The validity of this artificial immune controller is verified by simulation. Second, a new type of artificial immune controllers is constructed according to a simple double-cell immune dynamics model. The non-error characteristic of SISO control systems, which constructed by the artificial immune controller, is proved by the nonlinear theory in this chapter. The I/O stability and no-error characteristic of the system are verified by simulations, which show that the kind of artificial immune system have good anti-lag capability. Third, the Varela immune network model has been improved based on which an artificial immune system is proposed. The odd linearization method of the non-linear system is used to prove the stability and non-error characteristic of the SISO system constructed by the artificial immune control system. Its I/O stability, non-error characteristic and strong anti-lag capability are also verified by simulation. Finally, based on the comparison of the three kinds of immune controllers, a general structure of the artificial immune controller is proposed. The further study on this field is indicated in this chapter lastly.

The Artificial Immune System (AIS) is a biologically inspired computation system based on specifically human immune system. AIS applications in last one decade have been developed to address the complex computational and engineering problems related to classification, optimisation and anomaly detection. Many investigations have been conducted to understand the principles of immune system to translate the knowledge into AIS applications. However, a clear understanding of principles and responses of immune system is still required for application of AIS to Swarm Robotics. This article after a review of AIS models and algorithms proposes an integration of AIS and Swarm Robotics by developing a very clear understanding of immune system structures and associated functions.

Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process

This work focuses on an aspect of artificial immune systems not yet addressed by research, and demonstrates that as immune system metaphors grow and evolve in complexity, overall network health can play a role in the vulnerability and strength of the network. Recent works have suggested the analogous role of the immune system to computer security systems. As artificial immune system research progresses, a growing number of components of the human immune system have been incorporated, raising opportunities for further research and understanding. In particular, this work focuses on the development and testing of an agent-based model of a complex network to demonstrate and evaluate the concept of comparative relative strength (CRS) in artificial immune systems. CRS stems from a comparison of the overall strength of the host entity of an immune system to individual agent strength, this case in an artificial immune system (AIS). This principle has been employed to facilitate selection in a model of an AIS. Using the development and testing of an agent-based model of a complex network to demonstrate and evaluate the concept of comparative relative strength (CRS), this research introduces this concept.

Biology has produced living creatures that exhibit remarkable fault tolerance. The immune system is one feature that enables this. The acquired immune system learns during the life of the individual to differentiate between self (that which is normally present) and non-self (that which is not normally present). This paper describes an artificial immune system (AIS) that is used as an error detection system and is applied to two different robot based applications; the immunization of a fuzzy controller for a Khepera robot that provides object avoidance and a control module of a BAE Systems RASCAL/sup TM/ robot. The AIS learns normal behavior (unsupervised) during a fault free learning period and then identifies all error greater that a preset error sensitivity. The AIS was implemented in software but has the potential to be implemented in hardware. The AIS can be independent to the system under test, just requiring the inputs and outputs. This is not only ideal in terms of common mode and design errors but also offers the potential of a general, off-the-shelf, error detection system; the same AIS was applied to both the applications.

Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or, modelling biologically plausible dynamical systems, with little overlap between. We propose that this dichotomy is somewhat to blame for the lack of significant advancement of the field in either direction and demonstrate how a simplistic interpretation of Perelson’s shape-space formalism may have largely contributed to this dichotomy. In this paper, we motivate and derive an alternative representational abstraction. To do so we consider the validity of shape-space from both the biological and machine learning perspectives. We then take steps towards formally integrating these perspectives into a coherent computational model of notions such as life-long learning, degeneracy, constructive representations and contextual recognition—rhetoric that has long inspired work in AIS, while remaining largely devoid of operational definition.

Suggests that immune algorithms can be used effectively for the tuning of PID control structures for nonlinear processes. The controller's attribute behavior mechanism in the plant and the artificial immune system have certain similarities, since both systems deal with various attribute inputs and outputs through interactions among multiple-attribute modules. Since antibodies communicate with each other among different species of antibodies/B-cells through the stimulation and suppression chains among the antibodies that form a large-scale network, the artificial immune network system always has a new parallel decentralized processing mechanism for various situations. In addition to that, the structure of the network is not fixed but varies continuously, i.e. the artificial immune network flexibly self-organizes according to dynamic changes of the external environment. On the other hand, a number of tuning methods on the PID controller have been considered but, with only the P, I and D parameters, it is very difficult to control a plant with complex dynamics, such as large dead time, inverse response and highly nonlinear characteristics. An possibility of applying the flexible arbitration abilities of an artificial immune network has been suggested for the PID controller tuning. Simulation results reveal that immune network algorithms are effective for searching for optimal control against disturbances.