A dependency-based search strategy for feature selection

A dependency-based search strategy for feature selection

Feature selection has become an increasingly important field of research. It aims at finding optimal feature subsets that can achieve better generalization on unseen data. However, this can be a very challenging task, especially when dealing with large feature sets. Hence, a search strategy is needed to explore a relatively small portion of the search space in order to find ”semi-optimal” subse...

Exploring Automatic Feature Selection for Transition-Based Dependency Parsing

In this paper we investigate automatic techniques for finding an optimal feature model in the case of transition-based dependency parsing. We show a comparative study making a distinction between search algorithms, validation and decision rules demonstrating at the same time that using our methods it is possible to come up with quite complex feature specifications which are able to provide bett...

Automatic Feature Selection for Agenda-Based Dependency Parsing

In this paper we present an in-depth study on automatic feature selection for beam-search dependency parsers. The search strategy is inherited from the one implemented in MaltOptimizer, but searches in a much larger set of feature templates that could lead to a higher number of combinations. Our models provide results that are on par with models trained with a larger set of feature templates, a...

Dependency-based feature selection for clustering symbolic data

An Improved K-Nearest Neighbor with Crow Search Algorithm for Feature Selection in Text Documents Classification

The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...