Semi - naive bayesian classifier extends the structure of naive bayesian classifier in order to get rid of the limit of the assumption of independence between feature attributes of naive bayesian classifier and improve the performance of classification 半樸素貝葉斯分類模型對(duì)樸素貝葉斯分類模型的結(jié)構(gòu)進(jìn)行了擴(kuò)展,其目的是為了突破樸素貝葉斯分類模型特征屬性間獨(dú)立性假設(shè)限制,提高分類性能。
Bayesian classification is based on bayesian theorem . it can be comparable in interpretability with decision tree and in speed with neural network classifiers . bayesian classifiers have also exhibited high accuracy and speed when applied to large databases 該算法基于貝葉斯定理,可解釋性方面可以與判定樹相比,準(zhǔn)確度可和神經(jīng)網(wǎng)絡(luò)分類算法相媲美,用于大型數(shù)據(jù)庫(kù)時(shí)該算法已表現(xiàn)出高準(zhǔn)確度與高速度。
Specifically , aiming at two widely used algorithms in data mining , naive bayesian classifier and boolean association apriori algorithm . we have brought forward two corresponding protocols incorporating privacy concerns . we have used secure multi - party computation protocols and tools to get the solutions 本文針對(duì)數(shù)據(jù)挖掘中應(yīng)用較為廣泛的樸素貝葉斯分類器和關(guān)聯(lián)規(guī)則的apriori算法,利用安全多方計(jì)算的理論和工具,給出了與其相應(yīng)的隱私性算法。