Prediction of Customer Satisfaction Using Naive Bayes, MultiClass Classifier, K-Star and IBK.
Sanjiban Sekhar RoyDeeksha KaulReetika RoyCornel BarnaSuhasini MehtaAnusha MisraPublished in: SOFA (2) (2016)
Keyphrases
- multi class
- customer satisfaction
- naive bayes
- class probabilities
- feature selection
- naive bayes classifier
- multiclass classification
- decision trees
- cost sensitive
- binary classifiers
- support vector machine
- text classifiers
- classification algorithm
- training data
- base classifiers
- classification accuracy
- service quality
- text classification
- text categorization
- probability estimation
- prediction accuracy
- error correcting output codes
- multi class classification
- conditional independence assumption
- logistic regression
- probability estimates
- ensemble classifier
- binary classification
- binary classification problems
- support vector
- multi class problems
- information gain
- misclassification costs
- cost sensitive learning
- bayesian network classifiers
- bayesian networks
- feature space
- decision boundary
- learning algorithm
- svm classifier
- data sets
- pairwise
- ensemble learning
- feature extraction
- feature set
- k nearest neighbor
- machine learning algorithms
- classification error
- random forest