ICML 2001 - PDF Preprint Papers

ICML 2001 Workshop on Statistical Network Analysis

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ICML
2001
IEEE

425views Machine Learning» more ICML 2001»

Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection

14 years 6 months ago

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In this paper, we examine the advantages and disadvantages of filter and wrapper methods for feature selection and propose a new hybrid algorithm that uses boosting and incorporat...

Sanmay Das

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Id	Views	Title	Status

1	425	Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection	claim
2	400	Toward Optimal Active Learning through Sampling Estimation of Error Reduction	claim
3	296	Pairwise Comparison of Hypotheses in Evolutionary Learning	claim
4	266	Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data	claim
5	251	Learning to Select Good Title Words: An New Approach based on Reverse Information Retrieval	claim
6	203	Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers	claim
7	188	Feature selection for high-dimensional genomic microarray data	claim
8	185	Off-Policy Temporal Difference Learning with Function Approximation	claim
9	173	Some Theoretical Aspects of Boosting in the Presence of Noisy Data	claim
10	172	Continuous-Time Hierarchical Reinforcement Learning	claim
11	168	Using EM to Learn 3D Models of Indoor Environments with Mobile Robots	claim
12	165	Multiple Instance Regression	claim
13	164	Learning with the Set Covering Machine	claim
14	162	An Improved Predictive Accuracy Bound for Averaging Classifiers	claim
15	159	Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density	claim
16	159	Direct Policy Search using Paired Statistical Tests	claim
17	157	Bayesian approaches to failure prediction for disk drives	claim
18	155	Learning Probabilistic Models of Relational Structure	claim
19	154	Estimating a Kernel Fisher Discriminant in the Presence of Label Noise	claim
20	151	Breeding Decision Trees Using Evolutionary Techniques	claim
21	149	Constrained K-means Clustering with Background Knowledge	claim
22	146	A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal Difference Learning	claim
23	145	Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent Learning	claim
24	143	Inducing Partially-Defined Instances with Evolutionary Algorithms	claim
25	143	Scaling Reinforcement Learning toward RoboCup Soccer	claim
26	139	Multiple-Instance Learning of Real-Valued Data	claim
27	138	Hypertext Categorization using Hyperlink Patterns and Meta Data	claim
28	137	Latent Semantic Kernels	claim
29	137	Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation	claim
30	132	Expectation Maximization for Weakly Labeled Data	claim
31	130	On No-Regret Learning, Fictitious Play, and Nash Equilibrium	claim
32	130	Learning to Generate Fast Signal Processing Implementations	claim
33	129	General Loss Bounds for Universal Sequence Prediction	claim
34	127	Convergence of Gradient Dynamics with a Variable Learning Rate	claim
35	127	A Unified Loss Function in Bayesian Framework for Support Vector Regression	claim
36	126	Round Robin Rule Learning	claim
37	123	Coupled Clustering: a Method for Detecting Structural Correspondence	claim
38	110	Discovering Communicable Scientific Knowledge from Spatio-Temporal Data	claim

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