Bagging for One-Class Learning
نویسنده
چکیده
Consider the following outlier detection problem: suppose you are given an unlabeled data set and make the assumptions that one particular class is well-represented but you have no prior knowledge on how many outliers it contains. This scenario can arise in a variety of real world applications such as detecting intrusions in a network or spotting malignant tumors in medical images. Although constructing labels for the data is rarely impossible, in many cases, it may be cost prohibitive or inefficient.
منابع مشابه
Bagged One-Class Classifiers in the Presence of Outliers
The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as ...
متن کاملارتقای کیفیت دستهبندی متون با استفاده از کمیته دستهبند دو سطحی
Nowadays, the automated text classification has witnessed special importance due to the increasing availability of documents in digital form and ensuing need to organize them. Although this problem is in the Information Retrieval (IR) field, the dominant approach is based on machine learning techniques. Approaches based on classifier committees have shown a better performance than the others. I...
متن کاملAn Investigation of Sensitivity on Bagging Predictors: An Empirical Approach
As growing numbers of real world applications involve imbalanced class distribution or unequal costs for misclassification errors in different classes, learning from imbalanced class distribution is considered to be one of the most challenging issues in data mining research. This study empirically investigates the sensitivity of bagging predictors with respect to 12 algorithms and 9 levels of c...
متن کاملActively Balanced Bagging for Imbalanced Data
Under-sampling extensions of bagging are currently the most accurate ensembles specialized for class imbalanced data. Nevertheless, since improvements of recognition of the minority class, in this type of ensembles, are usually associated with a decrease of recognition of majority classes, we introduce a new, two phase, ensemble called Actively Balanced Bagging. The proposal is to first learn a...
متن کاملWhy Does Bagging Work? A Bayesian Account and its Implications
The error rate of decision-tree and other classi-cation learners can often be much reduced by bagging: learning multiple models from bootstrap samples of the database, and combining them by uniform voting. In this paper we empirically test two alternative explanations for this, both based on Bayesian learning theory: (1) bagging works because it is an approximation to the optimal procedure of B...
متن کاملEmpirical Study of Bagging Predictors on Medical Data
This study investigates the performance of bagging in terms of learning from imbalanced medical data. It is important for data miners to achieve highly accurate prediction models, and this is especially true for imbalanced medical applications. In these situations, practitioners are more interested in the minority class than the majority class; however, it is hard for a traditional supervised l...
متن کامل