Key Instance Detection in Multi-Instance Learning
نویسندگان
چکیده
The goal of traditional multi-instance learning (MIL) is to predict the labels of the bags, whereas in many real applications, it is desirable to get the instance labels, especially the labels of key instances that trigger the bag labels, in addition to getting bag labels. Such a problem has been largely unexplored before. In this paper, we formulate the Key Instance Detection (KID) problem, and propose a voting framework (VF) solution to KID. The key of VF is to exploit the relationship among instances, represented by a citer kNN graph. This graph is different from commonly used nearest neighbor graphs, but is suitable for KID. Experiments validate the effectiveness of VF for KID. Additionally, VF also outperforms state-of-the-art MIL approaches on the performance of bag label prediction.
منابع مشابه
IRDDS: Instance reduction based on Distance-based decision surface
In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...
متن کاملMulti-Instance Learning with Key Instance Shift
Multi-instance learning (MIL) deals with the tasks where each example is represented by a bag of instances. A bag is positive if it contains at least one positive instance, and negative otherwise. The positive instances are also called key instances. Only bag labels are observed, whereas specific instance labels are not available in MIL. Previous studies typically assume that training and test ...
متن کاملMultiple Instance Learning with the Optimal Sub-Pattern Assignment Metric
Multiple instance data are sets or multi-sets of unordered elements. Using metrics or distances for sets, we propose an approach to several multiple instance learning tasks, such as clustering (unsupervised learning), classification (supervised learning), and novelty detection (semi-supervised learning). In particular, we introduce the Optimal Sub-Pattern Assignment metric to multiple instance ...
متن کاملExperiments with Multi-view Multi-instance Learning for Supervised Image Classification
In this paper we empirically investigate the benefits of multi-view multi-instance (MVMI) learning for supervised image classification. In multi-instance learning, examples for learning contain bags of feature vectors and thus data from different views cannot simply be concatenated as in the singleinstance case. Hence, multi-view learning, where one classifier is built per view, is particularly...
متن کاملFAMER: Making Multi-Instance Learning Better and Faster
Kernel method is a powerful tool in multi-instance learning. However, many typical kernel methods for multi-instance learning ignore the correspondence information of instances between two bags or co-occurrence information, and result in poor performance. Additionally, most current multiinstance kernels unreasonably assign all instances in each bag an equal weight, which neglects the significan...
متن کامل