How to choose features for text classification?
Feature selection methods can be classified into 4 categories. Filter, Wrapper, Embedded , and Hybrid methods. Filter perform a statistical analysis over the feature space to select a discriminative subset of features.
Why feature selection is needed for document classification?
Feature selection serves two main purposes. First, it makes training and applying a classifier more efficient by decreasing the size of the effective vocabulary. This is of particular importance for classifiers that, unlike NB, are expensive to train.
How to evaluate feature selection methods for text classification?
A feature selection method with good classification performance may not necessarily have good stability and efficiency. So, the evaluation of feature selection methods for classifying small texts for text classification must consider multiple measures. We can model the evaluation as a multiple criteria decision-making (MCDM) problem [11].
Which is better feature selection or wrapper approach?
In the other hand Wrapper approach choose various subset of features are first identified then evaluated using classifiers. While The embedded approach the feature selection process is embedded into training phase of the classification. Hybrid approach takes advantages of both filter and wrapper approaches.
Which is the best filter for text classification?
Naive bayes is the popular one for text classification. [1] Labani, M., Moradi, P., Ahmadizar, F., Jalili, M., 2018. A novel multivariate filter method for feature selection in text classification problems. Engineering Applications of Artificial Intelligence.
How is feature selection used in data science?
Feature selection has been a research topic for decades, it is used in many fields such as bioinformatics, image recognition, image retrieval, text mining, etc. Theoretically, feature selection methods can be based on statistics, information theory, manifold, and rough set. Feature selection methods can be classified into 4 categories.