TY - GEN
T1 - Outlier Detection with Supervised Learning Method
AU - Bawono, Aditya Hari
AU - Bachtiar, Fitra Abdurrachman
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Outliers are data points that can affect the quality of data and the results of analysis from data mining. Outlier detection can also be seen as a pre-processing step to find data points that do not properly placed in the data set. Previously outlier detection methods are unsupervised. Today, ODDS provide data set with outlier information as a ground truth for supervised learning. However, outliers are commonly minor in any data set. For supervised learning this will lead to an imbalanced data classification problem. This paper presents the result of popular classification method, k-Nearest neighbor, Centroid Classifier, and Naive Bayes to handle outlier detection task. Even the mentioned methods were not designed to detect outlier, they proved by achieving 81% average sensitivity which is good for further research.
AB - Outliers are data points that can affect the quality of data and the results of analysis from data mining. Outlier detection can also be seen as a pre-processing step to find data points that do not properly placed in the data set. Previously outlier detection methods are unsupervised. Today, ODDS provide data set with outlier information as a ground truth for supervised learning. However, outliers are commonly minor in any data set. For supervised learning this will lead to an imbalanced data classification problem. This paper presents the result of popular classification method, k-Nearest neighbor, Centroid Classifier, and Naive Bayes to handle outlier detection task. Even the mentioned methods were not designed to detect outlier, they proved by achieving 81% average sensitivity which is good for further research.
KW - classification
KW - outlier
KW - outlier detection
KW - sensitivity
KW - supervised learning
UR - https://www.scopus.com/pages/publications/85080140517
U2 - 10.1109/SIET48054.2019.8986101
DO - 10.1109/SIET48054.2019.8986101
M3 - Conference contribution
AN - SCOPUS:85080140517
T3 - Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019
SP - 306
EP - 309
BT - Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019
Y2 - 28 September 2019 through 30 September 2019
ER -