The English language abounds in equivocal words; and there has been muchdiscussion on the cautious use of them so as to avoid unintentional ambiguity. Muchattention,however,remains to be paid to ambiguous consturction...The English language abounds in equivocal words; and there has been muchdiscussion on the cautious use of them so as to avoid unintentional ambiguity. Muchattention,however,remains to be paid to ambiguous consturction, whichperhaps leads to the greatest number of misconceptions. This paper is then devotedto a survey of the ambiguous construction characterized by careless placing of aword of phrase, the ambiguous arrangement of clauses in a sentence, theincautious omission of words and unclear pronoun reference; In English, as in Chinese. the proper usage consists in the proper words展开更多
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
文摘The English language abounds in equivocal words; and there has been muchdiscussion on the cautious use of them so as to avoid unintentional ambiguity. Muchattention,however,remains to be paid to ambiguous consturction, whichperhaps leads to the greatest number of misconceptions. This paper is then devotedto a survey of the ambiguous construction characterized by careless placing of aword of phrase, the ambiguous arrangement of clauses in a sentence, theincautious omission of words and unclear pronoun reference; In English, as in Chinese. the proper usage consists in the proper words
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.
