NDLI: Classification and clustering for neuroinformatics: Assessing the efficacy on reverse-mapped NeuroNLP data using standard ML techniques

Content Provider	IEEE Xplore Digital Library
Author	Melethadathil, N. Chellaiah, P. Nair, B. Diwakar, S.
Copyright Year	2015
Description	Author affiliation: Amrita Sch. of Biotechnol., Amrita Vishwa Vidyapeetham, Kollam, India (Melethadathil, N.; Chellaiah, P.; Nair, B.; Diwakar, S.)
Abstract	NeuroinformaticsNatural Language Processing (NeuroNLP) relies on clustering and classification for information categorization of biologically relevant extraction targets and for interconnections to knowledge-related patterns in event and text mined datasets. The accuracy of machine learning algorithms depended on quality of text-mined data while efficacy relied on the context of the choice of techniques. Although developments of automated keyword extraction methods have made differences in the quality of data selection, the efficacy of the Natural Language Processing (NLP) methods using verified keywords remain a challenge. In this paper, we studied the role of text classification and document clustering algorithms on datasets, where features were obtained by mapping to manually verified MESH terms published by National Library of Medicine (NLM). In this study, NLP data classification involved comparing 8techniques and unsupervised learning was performed with 6 clustering algorithms. Most classification techniques except meta-based algorithms namely stacking and vote, allowed 90% or higher training accuracy. Test accuracy was high (=>95%) probably due to limited test dataset. Logistic Model Trees had 30-fold higher runtime compared to other classification algorithms including Naive Bayes, AdaBoost, Hoeffding Tree. Grouped error rate in clustering was 0-4%. Runtime-wise, clustering was faster than classification algorithms on MESH-mapped NLP data suggesting clustering methods as adequate towards Medline-related datasets and text-mining big data analytic systems.
Starting Page	1065
Ending Page	1070
File Size	1662570
Page Count	6
File Format	PDF
ISBN	9781479987900
e-ISBN	9781479987924
DOI	10.1109/ICACCI.2015.7275751
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-10
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Error analysis Text categorization NeuroNLP Clustering algorithms Classification Neuroinformatics Filtering algorithms Biology Classification algorithms Clustering
Content Type	Text
Resource Type	Article

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