Open AccessADAPTIVE HUMAN MACHINE INTERACTION APPROACH FOR FEATURE SELECTION-EXTRACTION TASK IN MEDICAL DATA MINING
Author(s) -
Iryna Perova,
Yevgeniy Bodyanskiy
Publication year - 2018
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.17.2.997
Subject(s) - computer science , feature selection , principal component analysis , feature extraction , task (project management) , artificial intelligence , data mining , artificial neural network , selection (genetic algorithm) , feature (linguistics) , information extraction , machine learning , pattern recognition (psychology) , component (thermodynamics) , engineering , linguistics , philosophy , physics , systems engineering , thermodynamics
Feature Selection task is one of the most complicated and actual in the areas of Data Mining and Human Machine Interaction. Many approaches to its solving are based on non-mathematical and presentative hypothesis. New approach to evaluation of medical features information quantity, based on optimized combination of feature selection and feature extraction methods is proposed. This approach allows us to produce optimal reduced number of features with linguistic interpreting of each of them. Hybrid system of feature selection/extraction based on Neural Network-Physician interaction is investigated. This system is numerically simple, can produce feature selection/extraction with any number of factors in online mode using neural network-physician interaction based on Oja’s neurons for online principal component analysis and calculating distance between first principal component and all input features. A series of experiments confirms efficiency of proposed approaches in Medical Data Mining area and allows physicians to have the most informative features without losing their linguistic interpreting.