Abstract: In multi-instance learning, the training set is composed of labeled bags, each of which consists of many unla- beled instances,and the goal is to learn some classifier from the training set for correctly labeling unseen bags. In the past, some researches about multi-instance learning aim at improving single-instance learning algorithms to meet the multi-instance representation,and others try to propose some new methods to find the relationship between instances and bags and use the result to solve the problem. This paper started from adapting the representation of the bag and proposed a new algorithm-concept evaluating algorithm. First, this algorithm uses a cluster algorithm to cluster all instances into d group,here each group can be treated as a concept in the instances. Then,it uses the TF-IDF (term fre- qucncy-inverse document frequcncy)algorithm to get the importance of each concept in the bag. Finally, each bag is re represented as a d dimensional vector}concept evaluating vector, the ith value in this vector is the importance of the ith group in the bag. Because after re-representing the data set is not "multi" again, some propositional singl}instance learning algorithms can be used to solve multi-instance learning problem effetely.

Key words: Multi-instance learning, Rcrcprcscnt, Singlcinstancc learning, Concept evaluating