Kestrel-based Search Algorithm (KSA) and Long Short Term Memory (LSTM) Network for feature selection in classification of high-dimensional bioinformatics datasets.
Israel Edem AgbehadjiRichard C. MillhamSimon James FongHongji YangPublished in: FedCSIS (2018)
Keyphrases
- feature selection
- recurrent neural networks
- long short term memory
- search algorithm
- high dimensional
- high dimensionality
- feature selection algorithms
- feature space
- machine learning
- classification accuracy
- feature set
- benchmark datasets
- support vector
- high dimension
- feature extraction
- dimension reduction
- high dimensional datasets
- dimensionality reduction
- text classification
- microarray datasets
- small sample
- discriminative features
- classification models
- gene expression data
- irrelevant features
- uci machine learning repository
- data mining
- high dimensional data
- decision trees
- data analysis
- image classification
- data sets
- class imbalanced
- small sample size
- support vector machine
- selection algorithm
- unsupervised learning
- support vector machine svm
- model selection
- svm classifier
- microarray data
- feature reduction
- redundant features
- classification method
- low dimensional
- biological data
- search space
- training set
- information technology
- feature subset
- similarity search