Using support vector machine with a hybrid feature selection method to the stock trend prediction
By Ming-Chi Lee. Expert systems with applications, 2009
300 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/f87ef70858775f62367786d621287336852a710a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Seungwoo Jeon
- Victor I. Chang
- Bonghee Hong
- Juanying Xie
- Arash Bahrammirzaee
- Chunxia Wang
- Jacinta Chan Phooi M’ng
- M. Mehralizadeh
In Electrical and Electronic Engineering
In Management Science and Operations Research
- Seungwoo Jeon
- Victor I. Chang
- Bonghee Hong
- Juanying Xie
- Arash Bahrammirzaee
- Chunxia Wang
- Jacinta Chan Phooi M’ng
- M. Mehralizadeh
Citations in the corpus
- A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “[244] Stock trend prediction SVM with F-score and F_SSFS BPNN along with three commonly used feature selection methods Hybrid models performs better”
- Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases (2011, methodology): “The original F -score is a simple filter technique which measures the discrimination of two sets of real numbers (Chen & Lin, 2005; Akay, 2009; Lee, 2009 .”
- Pattern graph tracking-based stock price prediction using big data (2018, methodology): “In [34], the authors developed a prediction model based on support vector machine (SVM) with a hybrid feature selection method, which finds the original input features, for NASDAQ Index direction and unlike the above paper, f-score is used as a selection factor.”