Effect of Nonlinear Resource Allocation on AIRS Classifier Accuracy
نویسندگان
چکیده
Artificial Immune Recognition System (AIRS) is most popular immune inspired classifier. It also has shown itself to be a competitive classifier. AIRS uses linear method to allocate resources. In this paper, two different nonlinear resource allocation methods apply to AIRS. Then new algorithms are tested on 8 benchmark datasets. Based on the results of experiments, one of them increases the accuracy of AIRS in the majority of cases.
منابع مشابه
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 nonlin...
متن کاملEffect of Fuzzy Resource Allocation Method on Airs Classifier Accuracy
Artificial Immune Recognition System (AIRS) is an immune inspired classifier that is comparable to many popular classifiers. Many researches have been conducted to improve the accuracy of AIRS and to identify the significant components of AIRS that could empower it for better performance. Some of these researches have focused on the resource allocation component of AIRS. This study investigates...
متن کاملBreast 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 obtain...
متن کاملA Resource Limited Immune Approach for Evolving Architecture and Weights of Multilayer Neural Network
A resource limited immune approach (RLIA) was developed to evolve architecture and initial connection weights of multilayer neural networks. Then, with Back-Propagation (BP) algorithm, the appropriate connection weights can be found. The RLIA-BP classifier, which is derived from hybrid algorithm mentioned above, is demonstrated on SPOT multi-spectral image data, vowel data and Iris data effecti...
متن کاملBicriteria Resource Allocation Problem in Pert Networks
We develop a bicriteria model for the resource allocation problem in PERT networks, in which the total direct costs of the project as the first objective, and the mean of project completion time as the second objective are minimized. The activity durations are assumed to be independent random variables with either exponential or Erlang distributions, in which the mean of each activity duration ...
متن کامل