Dependency Bagging
نویسندگان
چکیده
In this paper, a new variant of Bagging named DepenBag is proposed. This algorithm obtains bootstrap samples at first. Then, it employs a causal discoverer to induce from each sample a dependency model expressed as a Directed Acyclic Graph (DAG). The attributes without connections to the class attribute in all the DAGs are then removed. Finally, a component learner is trained from each of the resulted samples to constitute the ensemble. Empirical study shows that DepenBag is effective in building ensembles of nearest neighbor classifiers.
منابع مشابه
HIT Dependency Parsing : Bootstrap Aggregating Heterogeneous Parsers
The paper describes our system of Shared Task on Parsing the Web. We only participate in dependency parsing task. A number of methods have been developed for dependency parsing. Each of the methods adopts very different view of dependency parsing, and each view can have its strengths and limitations. Thus system combination can have great potential to further improve the performance of dependen...
متن کاملRough Sets and Confidence Attribute Bagging for Chinese Architectural Document Categorization
Aiming at the problems of the traditional feature selection methods that threshold filtering loses a lot of effective architectural information and the shortcoming of Bagging algorithm that weaker classifiers of Bagging have the same weights to improve the performance of Chinese architectural document categorization, a new algorithm based on Rough set and Confidence Attribute Bagging is propose...
متن کاملIdentifying protein interaction subnetworks by a bagging Markov random field-based method
Identification of differentially expressed subnetworks from protein-protein interaction (PPI) networks has become increasingly important to our global understanding of the molecular mechanisms that drive cancer. Several methods have been proposed for PPI subnetwork identification, but the dependency among network member genes is not explicitly considered, leaving many important hub genes largel...
متن کاملThe NTU Toolkit and Framework for High-Level Feature Detection at TRECVID 2007
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...
متن کاملInvestigating the Effect of Underlying Fabric on the Bagging Behaviour of Denim Fabrics (RESEARCH NOTE)
Underlying fabrics can change the appearance, function and quality of the garment, and also add so much longevity of the garment. Nowadays, with the increasing use of various types of fabrics in the garment industry, their resistance to bagging is of great importance with the aim of determining the effectiveness of textiles under various forces. The current paper investigated the effect of unde...
متن کامل