Feature selection methods for big data bioinformatics: A survey from the search perspective
L Wang, Y Wang, Q Chang - Methods, 2016 - Elsevier
This paper surveys main principles of feature selection and their recent applications in big
data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and …
data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and …
[图书][B] Support vector machines: theory and applications
L Wang - 2005 - books.google.com
The support vector machine (SVM) has become one of the standard tools for machine
learning and data mining. This carefully edited volume presents the state of the art of the …
learning and data mining. This carefully edited volume presents the state of the art of the …
Bitcoin price prediction using ensembles of neural networks
E Sin, L Wang - 2017 13th International conference on natural …, 2017 - ieeexplore.ieee.org
This paper explores the relationship between the features of Bitcoin and the next day
change in the price of Bitcoin using an Artificial Neural Network ensemble approach called …
change in the price of Bitcoin using an Artificial Neural Network ensemble approach called …
Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery
Features extracted from real world applications increase dramatically, while machine
learning methods decrease their performance given the previous scenario, and feature …
learning methods decrease their performance given the previous scenario, and feature …
t-Test feature selection approach based on term frequency for text categorization
D Wang, H Zhang, R Liu, W Lv, D Wang - Pattern Recognition Letters, 2014 - Elsevier
Feature selection techniques play an important role in text categorization (TC), especially for
the large-scale TC tasks. Many new and improved methods have been proposed, and most …
the large-scale TC tasks. Many new and improved methods have been proposed, and most …
Rst-batminer: A fuzzy rule miner integrating rough set feature selection and bat optimization for detection of diabetes disease
Fuzzy classification rules are more interpretable and cope better with pervasive uncertainty
and vagueness with respect to crisp rules. Because of this fact, fuzzy classification rules are …
and vagueness with respect to crisp rules. Because of this fact, fuzzy classification rules are …
Rough set based feature selection: a review
JR Anaraki, M Eftekhari - The 5th Conference on Information …, 2013 - ieeexplore.ieee.org
Rough set is a tool with a mathematical foundation to deal with imprecise and imperfect
knowledge. It has been widely applied in machine learning, data mining and knowledge …
knowledge. It has been widely applied in machine learning, data mining and knowledge …
Hierarchical feature selection based on relative dependency for gear fault diagnosis
Feature selection is an important aspect under study in machine learning based diagnosis,
that aims to remove irrelevant features for reaching good performance in the diagnostic …
that aims to remove irrelevant features for reaching good performance in the diagnostic …
New feature selection paradigm based on hyper-heuristic technique
Feature selection (FS) is a crucial step for effective data mining since it has largest effect on
improving the performance of classifiers. This is achieved by removing the irrelevant …
improving the performance of classifiers. This is achieved by removing the irrelevant …
One-step and multi-step ahead stock prediction using backpropagation neural networks
G Dong, K Fataliyev, L Wang - 2013 9th International …, 2013 - ieeexplore.ieee.org
Forecasting stock price with traditional time series methods has proven to be difficult. An
artificial neural network is probably more suitable for this task, since no assumption of a …
artificial neural network is probably more suitable for this task, since no assumption of a …