The authorship attribution literature demonstrates the difficulty to design classifiers that outperform simple strategies such as linear classifiers operating on bag of features representation of documents. To overcome this difficulty we propose to use Bagging techniques that rely on learning classifiers on different random subsets of features, then to combine their decision by making them vote...

Boosting and Bagging, as two representative approaches to learning classiier committees, have demonstrated great success, especially for decision tree learning. They repeatedly build diierent classiiers using a base learning algorithm by changing the distribution of the training set. Sasc, as a diierent type of committee learning method, can also signiicantly reduce the error rate of decision t...

Missing values are an unavoidable issue of many real-world datasets. Dealing with missing values is an essential requirement in classification problem, because inadequate treatment with missing values often leads to large classification errors. Some classifiers can directly work with incomplete data, but they often result in big classification errors and generate complex models. Feature selecti...

Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of training data. A simple alternative to bagging is to partition the data into disjoint subsets. Experiments with decision tree and neural network classifiers on various datasets show that, given the same size partitions and bags, disjoint partitions result in performance equivalent to, or better tha...

-Classification is one of the data mining techniques that analyses a given data set and induces a model for each class based on their features present in the data. Bagging and boosting are heuristic approaches to develop classification models. These techniques generate a diverse ensemble of classifiers by manipulating the training data given to a base learning algorithm. They are very successfu...

In TRECVID 2007 high-level feature (HLF) detection, we extend the well-known LIBSVM and develop a toolkit specifically for HLF detection. The package shortens the learning time and provides a framework for researchers to easily conduct experiments. We efficiently and effectively aggregate detectors of training past data to achieve better performances. We propose post-processing techniques, conc...

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computation...