A selective sampling approach to active feature selection

Agrawal, 1993, Database mining: a performance perspective, IEEE Trans. Knowledge Data Engrg., 5, 914, 10.1109/69.250074 Agrawal, 1994, Fast algorithms for mining association rules, 487 Aha, 1991, Instance-based learning algorithms, Machine Learning, 6, 37, 10.1007/BF00153759 Bay Blake Blum, 1997, Selection of relevant features and examples in machine learning, Artificial Intelligence, 97, 245, 10.1016/S0004-3702(97)00063-5 Bradley, 1998, Scaling clustering algorithms to large databases, 9 Burges, 1998, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2, 121, 10.1023/A:1009715923555 Cochran, 1977 Cohn, 1994, Improving generalization with active learning, Machine Learning, 15, 201, 10.1007/BF00993277 Das, 2001, Filters, wrappers and a boosting-based hybrid for feature selection, 74 Dash, 1997, Feature selection for classification, Intelligent Data Analysis: An Internat. J., 1, 131, 10.3233/IDA-1997-1302 Dash, 2000, Feature selection for clustering, 110 Dash, 2000, Consistency based feature selection, 98 Dash, 1997, Dimensionality reduction of unsupervised data, 532 Dietterich, 1997, Machine learning research: four current directions, AI Magazine, 18, 97 Dy, 2000, Feature subset selection and order identification for unsupervised learning, 247 Dy, 2000, Visualization and interactive feature selection for unsupervised data, 360 Freund, 1997, Selective sampling using the query by committee algorithm, Machine Learning, 28, 133, 10.1023/A:1007330508534 Friedman, 1977, An algorithm for finding best matches in logarithmic expected time, ACM Trans. Math. Software, 3, 209, 10.1145/355744.355745 Gaede, 1998, Multidimensional access methods, ACM Comput. Surv., 30, 170, 10.1145/280277.280279 Gu, 2001, Sampling: knowing whole from its part, 21 M.A. Hall, Correlation based feature selection for machine learning, PhD Thesis, University of Waikato, Dept. of Computer Science, 1999 Hall, 2000, Correlation-based feature selection for discrete and numeric class machine learning Hong, 1997, Use of contextual information for feature ranking and discretization, IEEE Trans. Knowledge Data Engrg., 9, 718, 10.1109/69.634751 Joachims, 1998, Text categorization with support vector machines: learning with many relevant features, 137 Kim, 2000, Feature selection for unsupervised learning via evolutionary search, 365 Kira, 1992, The feature selection problem: traditional methods and a new algorithm, 129 Kira, 1992, A practical approach to feature selection, 249 Kivinen, 1994, The power of sampling in knowledge discovery, 77 Kohavi, 1997, Wrappers for feature subset selection, Artificial Intelligence, 97, 273, 10.1016/S0004-3702(97)00043-X Kononenko, 1994, Estimating attributes: Analysis and extension of RELIEF, 171 Kononenko, 1997, Overcoming the myopia of inductive learning algorithms with RELIEFF, Appl. Intelligence, 7, 39, 10.1023/A:1008280620621 Langley, 1994, Selection of relevant features in machine learning, 140 Leopold, 2002, Text categorization with support vector machines. How to represent texts in input space?, Machine Learning, 46, 423, 10.1023/A:1012491419635 Lewis, 1994, A sequential algorithm for training text classifiers, 3 Liu, 1998 2001 Liu, 2002, Feature selection with selective sampling, 395 Mitchell, 1997 A.W. Moore, An introductory tutorial on kd-trees, Extract from PhD Thesis Tech Report No. 209, Computer Laboratory, University of Cambridge, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 1991 Ng, 1998, On feature selection: learning with exponentially many irrelevant features as training examples, 404 Ng, 2000, Customer retention via data mining, AI Rev., 14, 569 Nigam, 2000, Text classification from labeled and unlabeled documents using EM, Machine Learning, 39, 103, 10.1023/A:1007692713085 Robnik-Sikonja, 1997, An adaptation of relief for attribute estimation in regression, 296 Robnik-Sikonja, 1999, Attribute dependencies, understandability and split selection in tree based models, 344 Robnik-Sikonja, 2001, Comprehensible interpretation of relief's estimates, 433 Robnik-Sikonja, 2003, Theoretical and empirical analysis of Relief and ReliefF, Machine Learning, 53, 23, 10.1023/A:1025667309714 Roy, 2001, Toward optimal active learning through sampling estimation of error reduction, 441 Schohn, 2000, Less is more: active learning with support vector machines, 839 Sikonja, 1998, Speeding up Relief algorithms with k–d trees Syed, 1999, A study of support vectors on model independent example selection, 272 Talavera, 1999, Feature selection as a preprocessing step for hierarchical clustering, 389 Thompson, 1999, Active learning for natural language parsing and information extraction, 406 Tong, 2001, Support vector machine active learning with applications to text classification, Machine Learning Res., 2, 45 Witten, 2000 Xing, 2001, Feature selection for high-dimensional genomic microarray data Yu, 2003, Feature selection for high-dimensional data: a fast correlation-based filter solution, 856