Results 11 to 20 of about 717,161 (269)

Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

open access: yesEntropy, 2016
Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings.
Jaesung Lee, Dae-Won Kim
doaj   +1 more source

Kurtosis-Based Feature Selection Method using Symmetric Uncertainty to Predict the Air Quality Index [PDF]

open access: yesComputer Science Journal of Moldova, 2022
Feature selection is vital in data pre-processing in machine learning, and it is prominent in datasets with many features. Feature selection analyses the relevant, irrelevant, and redundant features in the dataset.
Usharani Bhimavarapu, M. Sreedevi
doaj   +1 more source

Selecting Features with SVM [PDF]

open access: yes, 2013
A common problem with feature selection is to establish how many features should be retained at least so that important information is not lost. We describe a method for choosing this number that makes use of Support Vector Machines. The method is based on controlling an angle by which the decision hyperplane is tilt due to feature selection ...
Jacek Rzeniewicz, Julian Szymanski
openaire   +1 more source

Optimization for Gene Selection and Cancer Classification

open access: yesProceedings, 2021
Recently, gene selection has played an important role in cancer diagnosis and classification. In this study, it was studied to select high descriptive genes for use in cancer diagnosis in order to develop a classification analysis for cancer diagnosis ...
Hülya Başeğmez   +2 more
doaj   +1 more source

Topological Feature Selection

open access: yes, 2023
In this paper, we introduce a novel unsupervised, graph-based filter feature selection technique which exploits the power of topologically constrained network representations. We model dependency structures among features using a family of chordal graphs (the Triangulated Maximally Filtered Graph), and we maximise the likelihood of features' relevance ...
Antonio Briola, Tomaso Aste
openaire   +3 more sources

Data Attribute Selection with Information Gain to Improve Credit Approval Classification Performance using K-Nearest Neighbor Algorithm

open access: yesInternational Journal of Islamic Business and Economics (IJIBEC), 2017
Credit is one of the modern economic behaviors. In practice, credit can be either borrowing a certain amount of money or purchasing goods with a gradual payment process and within an agreed timeframe.
Ivandari Ivandari   +3 more
doaj   +1 more source

Dynamic Feature Selection for Clustering High Dimensional Data Streams

open access: yesIEEE Access, 2019
Change in a data stream can occur at the concept level and at the feature level. Change at the feature level can occur if new, additional features appear in the stream or if the importance and relevance of a feature changes as the stream progresses. This
Conor Fahy, Shengxiang Yang
doaj   +1 more source

Structure Preserving Non-negative Feature Self-Representation for Unsupervised Feature Selection

open access: yesIEEE Access, 2017
Inspired by the importance of self-representation and structure-preserving ability of features, in this paper, we propose a novel unsupervised feature selection algorithm named structure-preserving non-negative feature self-representation (SPNFSR).
Wei Zhou   +3 more
doaj   +1 more source

Feature extraction for epileptic seizure detection using machine learning

open access: yesCurrent Medicine Research and Practice, 2020
Background: Epilepsy is a common neurological disorder and affects approximately 70 million people worldwide. The traditional approach used by neurologists for the detection of seizure is time consuming.
Renuka Mohan Khati, Rajesh Ingle
doaj   +1 more source

New Feature Selection Algorithm Based on Feature Stability and Correlation

open access: yesIEEE Access, 2022
The analysis of a large amount of data with high dimensionality of rows and columns increases the load of machine learning algorithms. Such data are likely to have noise and consequently, obstruct the performance of machine learning algorithms.
Luai Al-Shalabi
doaj   +1 more source

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