Abstract
In medical field, class imbalance is one in every of the powerful issues in class of data mining, usually unbalanced
dataset found in varieties of applications and notably in medical centre caused from few styles of issues like uncertainty,
absence of information, imbalance, volumetric, misclassified value and degrades the performance of information mining in
terms of accuracy, cost, decision creating. K-Nearest Neighbor (KNN) and Cost Sensitive Learning algorithms with hybrid ways
used to solve the category imbalance disadvantage and to create superior result to existing solutions. Hospitalized datasets
square measure crazy imbalance quantitative relation is taken into thought from uci repository and mistreatment F-measure to
produce the accuracy, sensitive and confidence for patient data. And for future conceive to scale back the imbalance data issues.
In found multi-objective native search downside resolved mistreatment Pittsburgh model that is expensive and turn out rules for
little coverage.
Keywords
K-Nearest Neighbor (KNN)
Cost Sensitive Learning
F-measure
Classifier Level Approaches
Data Level Approaches
Authors
How to Cite this Article
S. Lavanya, Dr. S. Palaniswami, G. Kavinraj (2015).
"A REVIEW OF MULTICLASS PROBLEM AND SOLUTIONS FOR IMBALANCED DATASET".
International Journal of Contemporary Research in Computer Science and Technology,
1(8), pp. 294-297.
