Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Feature selection with conditional mutual information maximin in text categorization
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Algorithm Design
Boosting the Feature Space: Text Classification for Unstructured Data on the Web
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Neural Networks for Applied Sciences and Engineering
Neural Networks for Applied Sciences and Engineering
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Valid inequalities for mixed integer linear programs
Mathematical Programming: Series A and B
Asymmetric distance estimation with sketches for similarity search in high-dimensional spaces
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Local Probabilistic Models for Link Prediction
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
The slashdot zoo: mining a social network with negative edges
Proceedings of the 18th international conference on World wide web
Non-monotonic feature selection
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
New perspectives and methods in link prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
TAKES: a fast method to select features in the kernel space
Proceedings of the 20th ACM international conference on Information and knowledge management
Who will follow you back?: reciprocal relationship prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Link prediction: the power of maximal entropy random walk
Proceedings of the 20th ACM international conference on Information and knowledge management
Temporal link prediction by integrating content and structure information
Proceedings of the 20th ACM international conference on Information and knowledge management
PIKM 2012: 5th ACM workshop for PhD students in information and knowledge management
Proceedings of the 21st ACM international conference on Information and knowledge management
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Networks that model relationships in the real world have attracted much attention in the past few years. Link prediction plays a central role in the network area. Supervised learning is an important class of algorithms used to address the link prediction problem. A big challenge in solving link prediction tasks is deciding how to choose relevant features. As an important machine learning technique to select relevant features, feature selection not only enhances classification accuracy, but also improves the efficiency of the training process. Thus, it is especially relevant for link prediction. However, to the best of our knowledge, feature selection under the link prediction scenario remains unstudied. In this paper, we propose FEature Selection for Link Prediction (FESLP), which contains a feature ranking algorithm and a feature weighting algorithm to address link prediction tasks. We measure the discriminative ability of each individual feature and select those features with greatest discriminative power. Simultaneously, we aim to minimize the correlations among features such that redundancy in the learned feature space is as small as possible. Thus, the feature space can accurately preserve the sketch of the original data. Feature weighting and feature ranking problems can be formalized as two quadratic optimization problems. The active set method is used to solve the feature weighting problem (via real number programming) while a greedy policy is applied to solve the feature ranking problem (via integer programming). In experiments, We evaluate FESLP on six large-scale email network datasets from a university. The results show the effectiveness of the FESLP.