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JMLR Workshop and Conference Proceedings Volume 10: Feature Selection in Data Mining Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, June 21st, 2010, Hyderabad, India
Huan Liu,Hiroshi Motoda,Rudy Setiono,Zheng Zhao,Sanjay Chawla,Elham Salehi,Jayashree Nyayachavadi,Robin Gras +7 more
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The article was published on 2010-01-01 and is currently open access. It has received 12 citations till now.read more
Citations
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Journal ArticleDOI
Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers
TL;DR: It is demonstrated that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis and may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption.
Journal ArticleDOI
Intrusion detection using naive bayes classifier with feature reduction
Saurabh Mukherjee,Neelam Sharma +1 more
TL;DR: Empirical results show that selected reduced attributes give better performance to design IDS that is efficient and effective for network intrusion detection.
Journal ArticleDOI
A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results
Farideh Bagherzadeh-Khiabani,Azra Ramezankhani,Fereidoun Azizi,Farzad Hadaegh,Ewout W. Steyerberg,Davood Khalili +5 more
TL;DR: The experiment showed that the variable selection methods used in data mining could improve the performance of clinical prediction models.
Journal ArticleDOI
A Novel Multi-Classifier Layered Approach to Improve Minority Attack Detection in IDS☆
Neelam Sharma,Saurabh Mukherjee +1 more
TL;DR: A novel layered approach with multi-classifier is presented by combining naive Bayes classifier (NBC) and naive bayes tree (NBTree) to improve detection rate and precision of minority class without hurting the performance of majority class.
Proceedings ArticleDOI
Layered approach for intrusion detection using naïve Bayes classifier
Neelam Sharma,Saurabh Mukherjee +1 more
TL;DR: A layered approach for improving the minority attack detection rate without hurting the prediction performance of the majority attacks is proposed using Naive Bayes classifier on reduced dataset for each attack class.
References
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Proceedings Article
An Information-Theoretic Definition of Similarity
TL;DR: This work presents an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model and demonstrates how this definition can be used to measure the similarity in a number of different domains.
Journal ArticleDOI
A statistical interpretation of term specificity and its application in retrieval
TL;DR: It is argued that terms should be weighted according to collection frequency, so that matches on less frequent, more specific, terms are of greater value than matches on frequent terms.
Journal ArticleDOI
Toward memory-based reasoning
Craig Stanfill,David L. Waltz +1 more
TL;DR: The intensive use of memory to recall specific episodes from the past—rather than rules—should be the foundation of machine reasoning.
Book ChapterDOI
A Geometric Framework for Unsupervised Anomaly Detection
TL;DR: A new geometric framework for unsupervised anomaly detection is presented, which are algorithms that are designed to process unlabeled data to detect anomalies in sparse regions of the feature space.
Proceedings ArticleDOI
Similarity measures for categorical data: A comparative evaluation
TL;DR: In this paper, the performance of a variety of similarity measures in the context of a specific data mining task is evaluated. But their relative performance has not been evaluated for all types of problems.