Open AccessDiscriminant Pearson Correlative Feature Selection based Gentle Adaboost Classification for Medical Document Mining
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c5391.098319
Subject(s) - computer science , artificial intelligence , feature selection , linear discriminant analysis , correlative , pattern recognition (psychology) , selection (genetic algorithm) , adaboost , identification (biology) , document classification , data mining , machine learning , support vector machine , philosophy , linguistics , botany , biology
This paper examines Discriminant Pearson Correlative Analysis Based Multivariate Gentle Adaboost Classification (DPCA-MGAC) and it is used to improve the performance of medical document mining with minimum time complexity. A large number of documents are collected from PubMed databases through the semantic-based search. Processes such as removing stop words, stemming, features identification, selection of features i.e., relevant keywords for document classification are carried out. The significant feature selection is carried out using DPCA, and with the selected features the documents are categorized into different classes using MGAC. This classification process combines the results of all weak learners and makes a strong classification in order to improve the precision of medical data mining and minimizes the false positive rate. Experimental evaluation has been performed using PubMed database.