A comprehensive survey on feature selection in the various fields of machine learning
Abstract In Machine Learning (ML), Feature Selection (FS) plays a crucial part in reducing
data's dimensionality and enhancing any proposed framework's performance. However, in …
data's dimensionality and enhancing any proposed framework's performance. However, in …
Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review
MS Mirnaghi, F Haghighat - Energy and Buildings, 2020 - Elsevier
Abnormal operation of HVAC systems can result in an increase in energy usage as well as
poor indoor air quality, thermal discomfort, and low productivity. Building automated systems …
poor indoor air quality, thermal discomfort, and low productivity. Building automated systems …
Disentangled representation learning for multimodal emotion recognition
Multimodal emotion recognition aims to identify human emotions from text, audio, and visual
modalities. Previous methods either explore correlations between different modalities or …
modalities. Previous methods either explore correlations between different modalities or …
Am-gcn: Adaptive multi-channel graph convolutional networks
Graph Convolutional Networks (GCNs) have gained great popularity in tackling various
analytics tasks on graph and network data. However, some recent studies raise concerns …
analytics tasks on graph and network data. However, some recent studies raise concerns …
Similarity of neural network representations revisited
Recent work has sought to understand the behavior of neural networks by comparing
representations between layers and between different trained models. We examine methods …
representations between layers and between different trained models. We examine methods …
Multiclass feature selection with metaheuristic optimization algorithms: a review
Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …
selection is harder to perform since most classifications are binary. The feature selection …
Feature selection in machine learning: A new perspective
J Cai, J Luo, S Wang, S Yang - Neurocomputing, 2018 - Elsevier
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 …
machine learning and data mining. Feature selection provides an effective way to solve this …
A review of feature selection and its methods
B Venkatesh, J Anuradha - Cybernetics and information technologies, 2019 - sciendo.com
Nowadays, being in digital era the data generated by various applications are increasing
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …
Predicting Water Quality Index (WQI) by feature selection and machine learning: A case study of An Kim Hai irrigation system
A variety of water quality indices have been used to assess the state of waterbodies all over
the world. In calculating a Water Quality Index (WQI), traditional methods require the …
the world. In calculating a Water Quality Index (WQI), traditional methods require the …