Outlier ensembles
Tóm tắt
Ensemble analysis is a widely used meta-algorithm for many data mining problems such as classification and clustering. Numerous ensemble-based algorithms have been proposed in the literature for these problems. Compared to the clustering and classification problems, ensemble analysis has been studied in a limited way in the outlier detection literature. In some cases, ensemble analysis techniques have been implicitly used by many outlier analysis algorithms, but the approach is often buried deep into the algorithm and not formally recognized as a general-purpose meta-algorithm. This is in spite of the fact that this problem is rather important in the context of outlier analysis. This paper discusses the various methods which are used in the literature for outlier ensembles and the general principles by which such analysis can be made more effective. A discussion is also provided on how outlier ensembles relate to the ensemble-techniques used commonly for other data mining problems.
Từ khóa
Tài liệu tham khảo
Aggarwal C. C., 2013, CRC Press
Chawla N., 2003, SMOTEBoost: Improving prediction of the minority class in boosting, PKDD, 107
Domingos P., 2000, Bayesian Averaging of Classifiers and the Overfitting Problem. ICML Conference
Johnson T., 1998, ACM KDD Conference
Kriegel H., 2011, Interpreting and Unifying Outlier Scores. SDM Conference
Knorr E., 1998, Algorithms for Mining Distancebased Outliers in Large Datasets. VLDB Conference
Knorr E., 1999, Finding Intensional Knowledge of Distance-Based Outliers. VLDB Conference