A novel filter-wrapper hybrid greedy ensemble approach optimized using the genetic algorithm to reduce the dimensionality of high-dimensional biomedical datasets.
Tushaar GangavarapuNagamma PatilPublished in: Appl. Soft Comput. (2019)
Keyphrases
- high dimensional
- genetic algorithm
- high dimensional datasets
- feature selection
- high dimensionality
- dimensionality reduction
- low dimensional
- feature space
- high dimensional data
- information extraction
- neural network
- dimensional data
- high dimensional spaces
- variable selection
- multi dimensional
- nearest neighbor
- biomedical data
- parallel genetic algorithm
- wrapper method
- lower dimensional
- noise reduction
- parameter space
- fitness function
- genetic algorithm is employed
- kernel function
- similarity search
- data points
- dynamic programming
- data sets
- categorical attributes
- search algorithm
- evolutionary algorithm
- benchmark datasets
- greedy algorithm
- genetic programming
- high dimension
- high dimensional data space
- machine learning
- decision trees
- tabu search
- high dimensional data sets
- particle swarm optimization
- artificial neural networks
- input space
- biomedical images
- multi objective
- filtering algorithm
- fuzzy logic
- black box