Results 331 to 340 of about 6,871,211 (381)

Feature Selection

2006
Publisher Summary This chapter discusses feature selection and most of the well-known techniques used for it. Feature selection or feature reduction can be described as selecting the most important feature so as to reduce their number and at the same time retain as much as possible of their class discriminatory information.
Sergios Theodoridis, Konstantinos Koutroumbas   +1 more
openaire   +3 more sources

ON AUTOMATIC FEATURE SELECTION

International Journal of Pattern Recognition and Artificial Intelligence, 1988
We review recent research on methods for selecting features for multidimensional pattern classification. These methods include nonmonotonicity-tolerant branch-and-bound search and beam search. We describe the potential benefits of Monte Carlo approaches such as simulated annealing and genetic algorithms.
Jack Sklansky, W. Siedlecki
openaire   +2 more sources

Introduction to Feature Selection [PDF]

, 2017
This is an era of information. However, the data is only valuable if it is efficiently processed and useful information is derived out of it. It is now common to find applications that require data with thousands of attributes. Problem with processing such datasets is that they require huge amount of resources.
Usman Qamar, Muhammad Summair Raza
openaire   +1 more source

Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning

IEEE Transactions on Transportation Electrification, 2020
State of health (SOH) is a key parameter to assess lithium-ion battery feasibility for secondary usage applications. SOH estimation based on machine learning has attracted great attention in recent years and holds potentials for battery informatization ...
Xiaosong Hu, Yunhong Che, Xianke Lin, S. Onori   +3 more
semanticscholar   +1 more source

Feature Selection Based on Neighborhood Self-Information

IEEE Transactions on Cybernetics, 2020
The concept of dependency in a neighborhood rough set model is an important evaluation function for the feature selection. This function considers only the classification information contained in the lower approximation of the decision while ignoring the
Changzhong Wang, Y. Huang, Mingwen Shao, Qinghua Hu, De-gang Chen   +4 more
semanticscholar   +1 more source

Multiobjective Particle Swarm Optimization for Feature Selection With Fuzzy Cost

IEEE Transactions on Cybernetics, 2020
Feature selection (FS) is an important data processing technique in the field of machine learning. There have been various FS methods, but all assume that the cost associated with a feature is precise, which restricts their real applications. Focusing on
Ying Hu, Yong Zhang, D. Gong
semanticscholar   +1 more source

Feature Selection and Feature Engineering

2019
Feature selection and engineering are important steps in a machine learning pipeline and involves all the techniques adopted to reduce their dimensionality. Most of the time, these steps come after cleaning the dataset.
Mahmoud Hamdy, Hisham El-Amir
openaire   +2 more sources

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

Expert Syst. J. Knowl. Eng., 2020
Feature selection is a process aimed at filtering out unrepresentative features from a given dataset, usually allowing the later data mining and analysis steps to produce better results.
Chih-Wen Chen, Yi-Hong Tsai, F. Chang, Wei-Chao Lin   +3 more
semanticscholar   +1 more source

Feature Selection for Neural Networks Using Group Lasso Regularization

IEEE Transactions on Knowledge and Data Engineering, 2020
We propose an embedded/integrated feature selection method based on neural networks with Group Lasso penalty. Group Lasso regularization is considered to produce sparsity on the inputs to the network, i.e., for selection of useful features.
Huaqing Zhang, Jian Wang, Zhanquan Sun, J. Zurada, N. Pal   +4 more
semanticscholar   +1 more source

Unsupervised feature selection via multiple graph fusion and feature weight learning

Science China Information Sciences, 2023
Chang Tang, Xiao Zheng, Wei Zhang, Xinwang Liu, Xinzhong Zhu, En Zhu   +5 more
semanticscholar   +1 more source