Browsing by Author "Yeh, Wei-Chang"

Now showing 1 - 2 of 2

A hybrid immune-estimation distribution of algorithm for mining thyroid gland data
(2010-03-15)
Chang, Wei-Wen
;
Yeh, Wei-Chang
;
Huang, Pei-Chiao
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
In this paper we combine the main concepts of estimation of distribution algorithms (EDAs) and immune algorithms (IAs) to be a hybrid algorithm called immune-estimation of distribution algorithms (IEDA). Both EDAs and IAs are extended from genetic algorithms (GAs). EDAs eliminate the genetic operation including crossover and mutation from the GAs and places more emphasis on the relation between gene loci. It adopts the distribution of selected individuals in search space and models the probability distributions to generate the next population. However, the primary gap of EDAs is lock of diversity between individuals. Hence, we introduce the IAs that is a new branch in computational intelligence. The main concepts of IAs are suppression and hypermutation that make the individuals be more diversity. Moreover, the primary gap of IAs is to pay no attention to the relation between individuals. Therefore, we combine the main concepts of two algorithms to improve the gaps each other. The classification risk of data mining is applied by this paper and compares the results between IEDA and general GAs in the experiments. We adopt the thyroid gland data set from LICI databases. Based on the obtained results, our research absolute is better than general GAs including accuracy, type I error and type 11 error. The results show not only the excellence of accuracy but also the robustness of the proposed algorithm. In this paper we have got high quality results which can be used as reference for hospital decision making and research workers. (C) 2009 Elsevier Ltd. All rights reserved.
Particle Swarm Optimization Based Feature Selection in Mammogram Mass Classification
(2012)
Wong, Man To
;
He, Xiangjian
;
Hung Nguyen
;
Yeh, Wei-Chang
;
工業工程與管理學系
Mammography is currently the most effective method for early detection of breast cancer. This paper proposes an effective technique to classify regions of interests (ROIs) of digitized mammograms into mass and normal tissue regions by first finding the significant texture features of ROI using binary particle swarm optimization (BPSO). The data set used consisted of sixty-nine ROIs from the MIAS Mini-Mammographic database. Eighteen texture features were derived from the gray level co-occurrence matrix (GLCM) of each ROI. Significant features are found by a feature selection technique based on BPSO. The decision tree classifier is then used to classify the test set using these significant features. Experimental results show that the significant texture features found by the BPSO based feature selection technique can have better classification accuracy when compared to the full set of features. The BPSO feature selection technique also has similar or better performance in classification accuracy when compared to other widely used existing techniques.