Journal of Intelligent & Fuzzy Systems - Volume 38, issue 2

Authors: Liu, Donghai | Garg, Harish

Article Type: Research Article

Abstract:  Set pair analysis is a successful theory to deal with the certainty and uncertainty in the system based on “identity”-“discrepancy”-“contrary” degree of the connection number. It is very important to construct a reasonable connection number for handling the uncertainty between the feature of set pair. Considering the semantics of linguistic terms, we define a new approach to propose a linguistic connection number(LCN) corresponding to linguistic intuitionistic fuzzy number based on the set pair analysis theory and numerical scale function for semantics of linguistic terms. Then we define a cosine distance measure between the new LCNs and develop the TOPSIS method …to the proposed cosine distance measure. Furthermore, we give a numerical example of coal mine safety evaluation to illustrate the proposed method, and the sensitivity analysis of the ranking results is made based on different numerical scale functions. The feasibility and effectiveness of the proposed method are also verified by comparison with the existing methods. Show more

Keywords: Set pair analysis, numerical scale function, cosine distance measure, TOPSIS, linguistic intuitionistic fuzzy set

DOI: 10.3233/JIFS-191396

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 2369-2382, 2020

Authors: Ul Haq, Amin | Li, JianPing | Memon, Muhammad Hammad | Khan, Jalaluddin | Ud Din, Salah

Article Type: Research Article

Abstract: The effective detection of breast cancer is particularly essential for recovery and treatment in the initial phases. The existing methods are not successfully diagnosis breast cancer in the initial phases. Thus the initial recognition of breast cancer is expressively a great challenge for health professionals and scientists. To resolve the problem of initial stages recognition of breast cancer, we recommended a machine learning based diagnosis method which will excellently classify the malignant and benign persons. In the designing of our method machine learning model support vector machine has been applied to classify the malignant and benign persons. To increase the …classification performances of the method, we used Minimal Redundancy Maximal Relevance and Chi-square algorithms to choose more appropriate features from the breast cancer dataset. The training/testing splitting technique is used for training and testing of the model. Additionally, the performance of the model has been evaluated by performance assessment metrics. The experimental results demonstrated that the classifier support vector machine obtained best classification performance on the selected subset of features as selected by Minimal Redundancy Maximal Relevance feature selection algorithm. The performances of support vector machine on selected features by Chi square feature selection algorithm are low as compared to Minimal Redundancy Maximal Relevance algorithm. From experimental results analysis, we determined that the integrated system based on Minimal Redundancy Maximal Relevance and support vector machine performances are high due to the selection of more suitable features and obtained 99.71% accuracy. According to McNemar’s statistical test the proposed method is more significant then existing methods. Thus, we recommend that the proposed diagnosis method for effective detection of breast cancer. Show more

Keywords: Breast cancer, feature selection, machine learning predictive model, diagnosis system, classification

DOI: 10.3233/JIFS-191461

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 2383-2398, 2020