A feature selection approach based on term distributions

A feature selection approach based on term distributions

Feature selection has a direct impact on text categorization. Most existing algorithms are based on document level, and they haven't considered the influence of term frequency on text categorization. Based on these, we put forward a feature selection approach, FSATD, based on term distributions in the paper. In our proposed algorithm, three critical factors which are term frequency, the inter-c...

A Distributed Feature Selection Approach Based on a Complexity Measure

Feature selection is often required as a preliminary step for many machine learning problems. However, most of the existing methods only work in a centralized fashion, i.e. using the whole dataset at once. In this paper we propose a new methodology for distributing the feature selection process by samples which maintains the class distribution. Subsequently, it performs a merging procedure whic...

A Graph-Based Approach to Feature Selection

A Hypergraph-Based Approach to Feature Selection

In many data analysis tasks, one is often confronted with the problem of selecting features from very high dimensional data. The feature selection problem is essentially a combinatorial optimization problem which is computationally expensive. To overcome this problem it is frequently assumed that either features independently influence the class variable or do so only involving pairwise feature...

A Powerful Feature Selection approach based on Mutual Information

Feature selection aims to reduce the dimensionality of patterns for classificatory analysis by selecting the most informative instead of irrelevant and/or redundant features. In this paper we propose a novel feature selection measure based on mutual information and takes into consideration the interaction between features. The proposed measure is used to determine relevant features from the ori...