Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds
Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds
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DOI：
10.1007/s10822-007-9113-3
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发表时间：
2007-05-01
影响因子：
3.5
通讯作者：
Mitchell, John B. O.
中科院分区：
文献类型：
--
作者：
Cannon, Edward O.;Amini, Ata;Mitchell, John B. O.
我们结合朴素贝叶斯分类器（MP 2D），归纳逻辑编程（ILP）和支持向量归纳逻辑编程（SVILP）在一个标准的分子基准数据集，包括11个活动类和约102，000个结构的循环指纹的分类性能。朴素贝叶斯分类器独立处理特征，而ILP组合结构片段，然后创建具有更高预测能力的新特征。SVILP是最近提出的一种方法，它在普通ILP过程之后添加了支持向量机。方法的性能通过许多统计测量来评估，即召回率、特异性、精确度、F-测量、马修斯相关系数、受试者操作特征（ROC）曲线下的面积和富集因子（EF）。根据F-测量，它同时考虑到召回率和精度，SVILP是七个11类的上级方法。结果表明，贝叶斯分类器给出了最好的召回性能的11个目标中的8个，但有一个低得多的精度，特异性和F-措施。另一方面，SVILP模型仅对11个类别中的3个类别具有最高的召回率，但通常具有远远优于上级的特异性和精确性。为了评估SVILP优越性的统计学显著性，我们采用McNemar检验，该检验表明SVILP在11个活动类别中的6个类别中的表现显著优于其他两种方法（p < 5%），而在其余3个类别中具有较低显著性的上级优越性。虽然以前的贝叶斯分类器在分子分类研究中表现非常好，但这些结果表明SVILP能够从数据中提取额外的知识，从而进一步改善分类结果。
We investigate the classification performance of circular fingerprints in combination with the Naive Bayes Classifier (MP2D), Inductive Logic Programming (ILP) and Support Vector Inductive Logic Programming (SVILP) on a standard molecular benchmark dataset comprising 11 activity classes and about 102,000 structures. The Naive Bayes Classifier treats features independently while ILP combines structural fragments, and then creates new features with higher predictive power. SVILP is a very recently presented method which adds a support vector machine after common ILP procedures. The performance of the methods is evaluated via a number of statistical measures, namely recall, specificity, precision, F-measure, Matthews Correlation Coefficient, area under the Receiver Operating Characteristic (ROC) curve and enrichment factor (EF). According to the F-measure, which takes both recall and precision into account, SVILP is for seven out of the 11 classes the superior method. The results show that the Bayes Classifier gives the best recall performance for eight of the 11 targets, but has a much lower precision, specificity and F-measure. The SVILP model on the other hand has the highest recall for only three of the 11 classes, but generally far superior specificity and precision. To evaluate the statistical significance of the SVILP superiority, we employ McNemar's test which shows that SVILP performs significantly (p < 5%) better than both other methods for six out of 11 activity classes, while being superior with less significance for three of the remaining classes. While previously the Bayes Classifier was shown to perform very well in molecular classification studies, these results suggest that SVILP is able to extract additional knowledge from the data, thus improving classification results further.