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Stochastic Attribute Selection Committees
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1006/inco.1995.1136openalex-snapshot
Boosting a Weak Learning Algorithm by Majority
inferredcites →https://doi.org/10.1007/bfb0026690openalex-snapshot
Naive bayesian classifier committees
inferredcites →https://doi.org/10.1016/b978-0-444-88650-7.50030-5openalex-snapshot
Multiple decision trees
inferredcites →https://doi.org/10.1023/a:1007515423169openalex-snapshot
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
inferredcites →https://doi.org/10.1023/a:1018249309965openalex-snapshot
Error Reduction through Learning Multiple Descriptions
inferredcites →https://doi.org/10.1023/a:1022648800760openalex-snapshot
The Strength of Weak Learnability
inferredcites →https://doi.org/10.1080/095400996116839openalex-snapshot
Error Correlation and Error Reduction in Ensemble Classifiers
inferredcites →https://doi.org/10.1613/jair.105openalex-snapshot
Solving Multiclass Learning Problems via Error-Correcting Output Codes
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/3-540-36131-6_21openalex-snapshot
Shared Ensemble Learning Using Multi-trees
inferred← citeshttps://doi.org/10.1007/3-540-48219-9_24openalex-snapshot
Input Decimation Ensembles: Decorrelation through Dimensionality Reduction
inferred← citeshttps://doi.org/10.1007/3-540-48912-6_17openalex-snapshot
Stochastic Attribute Selection Committees with Multiple Boosting: Learning More Accurate and More Stable Classifier Committees
inferred← citeshttps://doi.org/10.1007/3-540-48912-6_50openalex-snapshot
Classifying Unseen Cases with Many Missing Values
inferred← citeshttps://doi.org/10.1007/978-3-319-19222-2_3openalex-snapshot
Ensemble Feature Selection for Rankings of Features
inferred← citeshttps://doi.org/10.1007/978-3-319-90315-6_15openalex-snapshot
A New Function for Ensemble Pruning
inferred← citeshttps://doi.org/10.1007/978-3-540-24581-0_62openalex-snapshot
Evaluating a Nearest-Neighbor Method to Substitute Continuous Missing Values
inferred← citeshttps://doi.org/10.1007/978-3-540-68416-9_15openalex-snapshot
Data Mining of Multi-categorized Data
inferred← citeshttps://doi.org/10.1007/978-3-540-78733-4_19openalex-snapshot
Categorized and Integrated Data Mining of Medical Data
inferred← citeshttps://doi.org/10.1007/978-981-99-3758-5_58openalex-snapshot
Naïve Bayesian Classifier Committee (NBCC) Using Decision Tree (C4.5) for Feature Selection
inferred← citeshttps://doi.org/10.1007/bfb0095254openalex-snapshot
Generating classifier committees by stochastically selecting both attributes and training examples
inferred← citeshttps://doi.org/10.1007/s11063-017-9619-1openalex-snapshot
Testing Different Ensemble Configurations for Feature Selection
inferred← citeshttps://doi.org/10.1016/j.asoc.2015.01.035openalex-snapshot
Distributed feature selection: An application to microarray data classification
inferred← citeshttps://doi.org/10.1016/j.compag.2016.03.026openalex-snapshot
A hybrid ensemble for classification in multiclass datasets: An application to oilseed disease dataset
inferred← citeshttps://doi.org/10.1016/j.compag.2020.105747openalex-snapshot
A particle swarm optimization based ensemble for vegetable crop disease recognition
inferred← citeshttps://doi.org/10.1016/j.neucom.2013.03.067openalex-snapshot
Data classification using an ensemble of filters
inferred← citeshttps://doi.org/10.1016/j.patcog.2011.06.006openalex-snapshot
An ensemble of filters and classifiers for microarray data classification
inferred← citeshttps://doi.org/10.1016/s0031-3203(02)00121-8openalex-snapshot
Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets
inferred← citeshttps://doi.org/10.1080/10255810305042openalex-snapshot
Bayes Error Rate Estimation Using Classifier Ensembles
inferred← citeshttps://doi.org/10.1109/fskd.2008.145openalex-snapshot
An Ensemble Classifier Based on Attribute Selection and Diversity Measure
inferred← citeshttps://doi.org/10.1109/icnc.2008.868openalex-snapshot
Active Learning for kNN Based on Bagging Features
inferred← citeshttps://doi.org/10.1109/tai.1998.744846openalex-snapshot
Integrating boosting and stochastic attribute selection committees for further improving the performance of decision tree learning
inferred← citeshttps://doi.org/10.1109/tkde.2004.29openalex-snapshot
Multistrategy ensemble learning: reducing error by combining ensemble learning techniques
inferred← citeshttps://doi.org/10.1515/9783110671124-017openalex-snapshot
17 Extremely Randomized Trees
inferred← citeshttps://doi.org/10.3233/ida-2002-6606openalex-snapshot
Missing values prediction with K2
inferred← citeshttps://doi.org/10.4018/978-1-60566-010-3.ch119openalex-snapshot
Ensemble Data Mining Methods
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