@article{MAKHILLAJIT20181726725,
    title = {Multi-Target Regression Prediction on Cervical Cancer for
Evaluation of Predictive Performance Measures},
    journal = {Asian Journal of Information Technology},
    volume = {17},
    number = {2},
    pages = {160-166},
    year = {2018},
    issn = {1682-3915},
    doi = {ajit.2018.160.166},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2018.160.166},
    author = {S.G.,A.S.,B.B. and},
    keywords = {Cervical cancer,multi-target variable,single target variable,multi-target regression,classifier,performance measures},
    abstract = {Multi-target prediction is a prediction that consider more than one target variable in a real-life
problems like cervical cancer simultaneously instead of the concentration of most researchers on supervised
learning that has to do with prediction of a single target variable. The framework for multiple target variables
has significant effect for categorization and evaluation that a single target variable framework cannot take care
of. In the findings in the course of this study we did not come across the use of multi-target regression
technique for predictive performance measure on cervical cancer dataset that predict all the target variables
simultaneously. In this study, we adopt the problem transformation approach using multi-target classifiers to
transform a binary classification task into a regression task. The predictive performance measures in supervised
learning for multi-target classification task employ in this study is evaluated using exact match, hamming loss,
hamming score, ZeroOne loss and accuracy per label. The findings of this study shows that the multi-target
classifier (Bayesian classifier chains) using decision stump (base classifier) gives the highest predictive
performance measures on hamming score, exact match, ZeroOne loss and accuracy per label compared to the
multi-target classifier (classifier chains and class relevant) using J48 and random forest (base classifier) using
10 folds cross-validation and training and testing evaluation option. In conclusion, this study support the
assertion made by some researchers that decision tree and random forest are powerful techniques for prediction.}
    }