Hybridizing Artificial Neural Networks Through Feature Selection Based Supervised Weight Initialization and Traditional Machine Learning Algorithms for Improved Colon Cancer Prediction.
Malik Sajjad Ahmed NadeemMuhammad Hammad WaseemWajid AzizUsman HabibAnum MasoodMuhammad Attique KhanPublished in: IEEE Access (2024)
Keyphrases
- machine learning algorithms
- feature selection
- artificial neural networks
- machine learning
- learning algorithm
- colon cancer
- standard machine learning algorithms
- benchmark data sets
- input features
- cancer classification
- learning problems
- machine learning methods
- gene selection
- decision trees
- microarray data
- prediction accuracy
- gene expression data
- cancer diagnosis
- learning tasks
- unsupervised learning
- statistical machine learning
- text categorization
- machine learning systems
- machine learning approaches
- neural network
- support vector
- machine learning models
- gene expression
- text classification
- feature set
- genetic algorithm ga
- mutual information
- prediction model
- information gain
- learning models
- feature selection algorithms
- support vector machine
- multi class
- microarray
- predictive model
- classification models
- supervised learning
- feature subset
- feature space
- k means
- high dimensional
- microarray datasets
- data sets
- feature ranking
- selected features
- high dimensionality