Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce.

ABSTRACT: Increasing demands for information and the rapid growth of big data have dramatically increased the amount of textual data. In order to obtain useful text information, the classification of texts is considered an imperative task. Accordingly, this article will describe the development of a hybrid optimization algorithm for classifying text. Here, pre-processing was done using the stemming process and stop word removal. Additionally, we performed the extraction of imperative features and the selection of optimal features using the Tanimoto similarity, which estimates the similarity between features and selects the relevant features with higher feature selection accuracy. Following that, a deep residual network trained by the Adam algorithm was utilized for dynamic text classification. Dynam

SUBMITTER: Abdalla HB 

PROVIDER: S-EPMC9044237 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature