انتخاب ویژگی شبکه های بیزی پیچیده الگوریتم ژنتیک کاربردی برای دیفرانسیل تشخیص بیماریهای erythemato-سنگفرشی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|29192||2013||8 صفحه PDF||سفارش دهید||6420 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Digital Signal Processing, Volume 23, Issue 1, January 2013, Pages 230–237
This paper presents a new method for differential diagnosis of erythemato-squamous diseases based on Genetic Algorithm (GA) wrapped Bayesian Network (BN) Feature Selection (FS). With this aim, a GA based FS algorithm combined in parallel with a BN classifier is proposed. Basically, erythemato-squamous dataset contains six dermatological diseases defined with 34 features. In GA–BN algorithm, GA makes a heuristic search to find most relevant feature model that increase accuracy of BN algorithm with the use of a 10-fold cross-validation strategy. The subsets of features are sequentially used to identify six dermatological diseases via a BN fitting the corresponding data. The algorithm, in this case, produces 99.20% classification accuracy in the diagnosis of erythemato-squamous diseases. The strength of feature model generated for BN is furthermore tested with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Simple Logistics (SL) and Functional Decision Tree (FT). The resultant classification accuracies of algorithms are 98.36%, 97.00%, 98.36% and 97.81% respectively. On the other hand, BN algorithm with classification accuracy of 99.20% is quite a high diagnosis performance for erythemato-squamous diseases. The proposed algorithm makes no more than 3 misclassifications out of 366 instances. Furthermore, FS power of GA is also compared with two alternative search algorithms, i.e. Best First (BF) and Sequential Floating (SF). The obtained results have all together shown that the proposed GA–BN based FS and prediction strategy is very promising in diagnosis of erythemato-squamous diseases.