… This paper proposes a new intelligent machine learning framework for … features set that
affects the outcomes of NBA games. We would like to identify whether machine learning methods …

… five surface-based MRI features and combined them in a machine learning model to classify
… More significantly, in patients that were MRI-negative, machine learning correctly identified …

… extraction, feature transformation, feature selection, and feature analysis and evaluation. …
feature engineering concepts and techniques, which are an important part of machine learning …

… predicted based on CT image features, especially for LUAD. Our findings extend the
application of machine learning algorithms in CT image feature prediction for pathologic staging …

… High-dimensional data analysis is a challenge for researchers and engineers in the fields
of machine learning and data mining. Feature selection provides an effective way to solve this …

… In this research work, the issue of feature … of feature selection methods for sentiment analysis
and attempts to find the synergy between feature selection methods and machine learning …

… Feature engineering is the act of extracting features from raw data and … the machine learning
model. It is a crucial step in the machine learning pipeline, because the right features can …

… In the field of machine learning and pattern recognition, … In this paper, some widely used
feature selection and feature … to achieve high performance of learning algorithms that ultimately …

… In addition, we have also added the feature analysis step (ie feature clustering, selection, …
Lastly, feature analysis is yet another crucial step in machine learning of sequence analysis. …

… method [17], the neural network-based learning method [10], [18], the probabilistic … ], the
machine learning approach using a boosted cascade of simple features [21], statistical learning …