Positive-Unlabeled Learning for Network Link Prediction
نویسندگان
چکیده
Link prediction is an important problem in network data mining, which dedicated to predicting the potential relationship between nodes network. Normally, link based on supervised classification will be trained a dataset consisting of set positive samples and negative samples. However, well-labeled training datasets with annotations are always inadequate real-world scenarios, contain large number unlabeled that may hinder performance model. To address this problem, we propose positive-unlabeled learning framework representation for only using We first learn vectors method. Next, concatenate node pairs then feed them into different classifiers predict whether exists or not. alleviate imbalance enhance precision, adopt three types (PU) strategies improve traditional classifier estimation, bagging strategy reliable sampling. conduct experiments compare PU methods discuss their influence results. The experimental results demonstrate has impact predictive performances promotion effects vary structures.
منابع مشابه
Positive-Unlabeled Learning for Pupylation Sites Prediction
Pupylation plays a key role in regulating various protein functions as a crucial posttranslational modification of prokaryotes. In order to understand the molecular mechanism of pupylation, it is important to identify pupylation substrates and sites accurately. Several computational methods have been developed to identify pupylation sites because the traditional experimental methods are time-co...
متن کاملPositive-unlabeled learning for disease gene identification
BACKGROUND Identifying disease genes from human genome is an important but challenging task in biomedical research. Machine learning methods can be applied to discover new disease genes based on the known ones. Existing machine learning methods typically use the known disease genes as the positive training set P and the unknown genes as the negative training set N (non-disease gene set does not...
متن کاملPositive Unlabeled Learning for Deceptive Reviews Detection
Deceptive reviews detection has attracted significant attention from both business and research communities. However, due to the difficulty of human labeling needed for supervised learning, the problem remains to be highly challenging. This paper proposed a novel angle to the problem by modeling PU (positive unlabeled) learning. A semi-supervised model, called mixing population and individual p...
متن کاملPositive Unlabeled Learning for Data Stream Classification
Learning from positive and unlabeled examples (PU learning) has been investigated in recent years as an alternative learning model for dealing with situations where negative training examples are not available. It has many real world applications, but it has yet to be applied in the data stream environment where it is highly possible that only a small set of positive data and no negative data i...
متن کاملEfficient Training for Positive Unlabeled Learning
Positive unlabeled learning (PU learning) refers to the task of learning a binary classifier from only positive and unlabeled data [1]. This problem arises in various practical applications, like in multimedia/information retrieval [2], where the goal is to find samples in an unlabeled data set that are similar to the samples provided by a user, as well as for applications of outlier detection ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mathematics
سال: 2022
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math10183345