Dealing with Missing Values in a Probabilistic Decision Tree during Classification.
Lamis HawarahAna SimonetMichel SimonetPublished in: ICDM Workshops (2006)
Keyphrases
- missing values
- decision trees
- missing data
- missing attribute values
- cost sensitive learning
- multivariate temporal data
- incomplete data
- multiple imputation
- support vector machine svm
- classification accuracy
- data imputation
- incomplete data sets
- data stream classification
- feature extraction
- unseen test data
- machine learning algorithms
- feature values
- machine learning
- missing data imputation
- pattern recognition
- training set
- feature selection
- probabilistic model
- training data
- imputation methods
- feature vectors
- classification rules
- logistic regression
- text classification
- statistical databases
- multiple types
- model selection
- classification algorithm
- informative features
- class labels
- image processing
- bayesian networks
- high dimensional data
- neural network
- support vector
- unseen data
- k nearest neighbor