Helmholtz principle based supervised and unsupervised feature selection methods for text mining

Abstract

Tutkan, Melike (Dogus Author) -- Akyokuş, Selim (Dogus Author)One of the important problems in text classification is the high dimensionality of the feature space. Feature selection methods are used to reduce the dimensionality of the feature space by selecting the most valuable features for classification. Apart from reducing the dimensionality, feature selection methods have potential to improve text classifiers' performance both in terms of accuracy and time. Furthermore, it helps to build simpler and as a result more comprehensible models. In this study we propose new methods for feature selection from textual data, called Meaning Based Feature Selection (MBFS) which is based on the Helmholtz principle from the Gestalt theory of human perception which is used in image processing. The proposed approaches are extensively evaluated by their effect on the classification performance of two well-known classifiers on several datasets and compared with several feature selection algorithms commonly used in text mining. Our results demonstrate the value of the MBFS methods in terms of classification accuracy and execution time

Different approaches for identifying important concepts in probabilistic biomedical text summarization

“…In data mining context, the Helmholtz principle states that essential features and interesting events are observed in large deviations from randomness [47]. In text mining research, the Helmholtz principle has been used for document processing and keyword extraction [12], automatic text summarization [48], and supervised and unsupervised feature selection [49]. The primary study [12] dealt with words to extract the meaningful units of a text document, but we deal with concepts instead.…”

Review of feature extraction approaches on biomedical text classification

“…While, Dadaneh et al (2016) mentioned that feature selection is one of the most important fields in pattern recognition, which aims to pick a subset of relevant and informative features from an original feature set. Many other researchers study on feature selection approaches to handle high dimensionality problem (Ghareb et al, 2016;Hernández-Pereira et al, 2016;Tutkan et al, 2016;Vinh et al, 2016;Feng et al, 2015;Pinheiro et al, 2015;Chandrashekar and Sahin, 2014;Inbarani et al, 2015;Khalid et al, 2014;Rehman et al, 2015;Javed et al, 2012;Maldonado and Weber, 2009;Wei and Billings, 2007). Although many feature selection approaches have been proposed and have been employed in various domains, there are still some issues, especially in retrieving the relevant documents.…”

“…However, their results show the Odds Ratio and Information Gain outperformed Chi-square for 20 Newsgroups dataset. In other similar research, Tutkan et al (2016) proposed a new feature selection method named Meaning Based Feature Selection (MBFS). Then, they compared the performance of their proposed feature selection method with other methods such as Information Gain, Chi-squared, Odds ratio.…”

Estimator learning automata for feature subset selection in high‐dimensional spaces, case study: Email spam detection

“…Today's large‐scale text data, our case study in this research, are also an important high‐dimensional application field where the space regularly contains at least several thousands of distinct terms and is very sparse and noisy. We focus on text classification, which is a useful subdiscipline of data mining and is an active research path nowadays . Given a set of text documents with known class labels, text classification intends to predict the class of new instances.…”