Mining from ambiguous data is very important in data mining. This paper discusses one of the tasks for mining from ambiguous data known as multi-instance problem. In multi-instance problem, each pattern is a labeled b...Mining from ambiguous data is very important in data mining. This paper discusses one of the tasks for mining from ambiguous data known as multi-instance problem. In multi-instance problem, each pattern is a labeled bag that consists of a number of unlabeled instances. A bag is negative if all instances in it are negative. A bag is positive if it has at least one positive instance. Because the instances in the positive bag are not labeled, each positive bag is an ambiguous. The mining aim is to classify unseen bags. The main idea of existing multi-instance algorithms is to find true positive instances in positive bags and convert the multi-instance problem to the supervised problem, and get the labels of test bags according to predict the labels of unknown instances. In this paper, we aim at mining the multi-instance data from another point of view, i.e., excluding the false positive instances in positive bags and predicting the label of an entire unknown bag. We propose an algorithm called Multi-Instance Covering kNN （MICkNN） for mining from multi-instance data. Briefly, constructive covering algorithm is utilized to restructure the structure of the original multi-instance data at first. Then, the kNN algorithm is applied to discriminate the false positive instances. In the test stage, we label the tested bag directly according to the similarity between the unseen bag and sphere neighbors obtained from last two steps. Experimental results demonstrate the proposed algorithm is competitive with most of the state-of-the-art multi-instance methods both in classification accuracy and running time.展开更多
提出了一种基于TCM-KNN的网络异常检测新方法,并采用遗传算法选择使用少量高质量的训练样本进行建模,从而有效地对入侵进行检测。大量基于著名的KDD Cup 1999数据集的实验表明:其相对于传统的异常检测方法在保证较高检测率的前提下,有...提出了一种基于TCM-KNN的网络异常检测新方法,并采用遗传算法选择使用少量高质量的训练样本进行建模,从而有效地对入侵进行检测。大量基于著名的KDD Cup 1999数据集的实验表明:其相对于传统的异常检测方法在保证较高检测率的前提下,有效地降低了误报率;并且,在采用选择后的训练集优化处理后,其性能没有明显的削减,因而相对于传统方法更为适用于现实的网络应用环境。展开更多
基金the National Natural Science Foundation of China (Nos. 61073117 and 61175046)the Provincial Natural Science Research Program of Higher Education Institutions of Anhui Province (No. KJ2013A016)+1 种基金the Academic Innovative Research Projects of Anhui University Graduate Students (No. 10117700183)the 211 Project of Anhui University
文摘Mining from ambiguous data is very important in data mining. This paper discusses one of the tasks for mining from ambiguous data known as multi-instance problem. In multi-instance problem, each pattern is a labeled bag that consists of a number of unlabeled instances. A bag is negative if all instances in it are negative. A bag is positive if it has at least one positive instance. Because the instances in the positive bag are not labeled, each positive bag is an ambiguous. The mining aim is to classify unseen bags. The main idea of existing multi-instance algorithms is to find true positive instances in positive bags and convert the multi-instance problem to the supervised problem, and get the labels of test bags according to predict the labels of unknown instances. In this paper, we aim at mining the multi-instance data from another point of view, i.e., excluding the false positive instances in positive bags and predicting the label of an entire unknown bag. We propose an algorithm called Multi-Instance Covering kNN （MICkNN） for mining from multi-instance data. Briefly, constructive covering algorithm is utilized to restructure the structure of the original multi-instance data at first. Then, the kNN algorithm is applied to discriminate the false positive instances. In the test stage, we label the tested bag directly according to the similarity between the unseen bag and sphere neighbors obtained from last two steps. Experimental results demonstrate the proposed algorithm is competitive with most of the state-of-the-art multi-instance methods both in classification accuracy and running time.
文摘提出了一种基于TCM-KNN的网络异常检测新方法,并采用遗传算法选择使用少量高质量的训练样本进行建模,从而有效地对入侵进行检测。大量基于著名的KDD Cup 1999数据集的实验表明:其相对于传统的异常检测方法在保证较高检测率的前提下,有效地降低了误报率;并且,在采用选择后的训练集优化处理后,其性能没有明显的削减,因而相对于传统方法更为适用于现实的网络应用环境。
