Results 211 to 220 of about 65,050 (262)

Machine Learning‐Assisted KCl‐CaCl2‐LiCl Electrolyte Design for Low‐Temperature, High‐Performance Calcium‐Based Liquid Metal Batteries

Advanced Science, EarlyView.
A machine learning‐assisted framework optimizes the KCl‐CaCl2‐LiCl ternary electrolyte. The optimized 13:35:52 mol% composition enables Ca‐based liquid metal batteries to operate stably at 480 °C, with >99.5% coulombic efficiency, ultralow self‐discharge, and excellent cycling stability, advancing low‐temperature large‐scale energy storage.
Xinglin Zhou, Lei Huang, Yan Zhou, Xiaohui Ning   +3 more
wiley   +1 more source

Circulating Amino Acid Network Remodeling Reveals Systemic Metabolic Reprogramming Predictive of Colorectal Cancer Recurrence and Metastasis

Advanced Science, EarlyView.
Blood‐based amino acid patterns measured by 19F NMR reveal hidden metabolic changes in colorectal cancer. By analyzing how these amino acids interact as a network, machine learning models identify patients at higher risk of recurrence and metastasis.
Ji‐Yeon Lee, Jumi Kim, Taehan Yoon, So Hyun Kwon, Su Chan Park, Dohyun Chun, Dohyeong Kim, Eun Jung Park, Hyunwoo Kim, Ji Min Lee   +9 more
wiley   +1 more source

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Improved Random Forest for Classification

IEEE Transactions on Image Processing, 2018
We propose an improved random forest classifier that performs classification with minimum number of trees. The proposed method iteratively removes some unimportant features. Based on the number of important and unimportant features, we formulate a novel theoretical upper limit on the number of trees to be added to the forest to ensure improvement in ...
Angshuman Paul, Dipti Prasad Mukherjee, Prasun Das   +2 more
exaly   +3 more sources

Cancer classification using Rotation Forest

Computers in Biology and Medicine, 2008
We address the microarray dataset based cancer classification using a newly proposed multiple classifier system (MCS), referred to as Rotation Forest. To the best of our knowledge, it is the first time that Rotation Forest has been applied to the microarray dataset classification.
Kun-Hong Liu, De-Shuang Huang
exaly   +3 more sources

Improving Classification Trustworthiness in Random Forests

2021 IEEE International Conference on Cyber Security and Resilience (CSR), 2021
Machine learning algorithms are becoming more and more widespread in industrial as well as in societal settings. This diffusion is starting to become a critical aspect of new software-intensive applications due to the need of fast reactions to changes, even if temporary, in data.
De Biase M. S., Marulli F., Verde L., Marrone S.   +3 more
openaire   +1 more source

Kernel Rotation Forests for Classification

2020 IEEE International Conference on Big Data and Smart Computing (BigComp), 2020
There have been significant research efforts for developing decision tree (DT)-based ensemble methods. Such methods generally construct an ensemble by aggregating a large number of unpruned DTs, thereby yielding good classification accuracy. A recently developed method, rotation forest, is known to achieve better classification accuracy by rotating the
Jaewoong Shim, Seokho Kang 0001, Sungzoon Cho   +2 more
openaire   +1 more source

An imprecise deep forest for classification

Expert Systems with Applications, 2020
Abstract An imprecise deep forest classifier, which can be regarded as a modification of the deep forest proposed by Zhou and Feng, is presented in the paper. In the proposed classifier, the precise class probabilities at leaf nodes of decision trees in the deep forest are replaced with interval-valued probabilities produced by Walley’s imprecise ...
openaire   +1 more source

Proactive Forest for Supervised Classification

2018
Random Forest is one of the most used and accurate ensemble methods based on decision trees. Since diversity is a necessary condition to build a good ensemble, Random Forest selects a random feature subset for building decision nodes. This generation procedure could cause important features to be selected in multiple trees in the ensemble, decreasing ...
Nayma Cepero-Pérez, Luis Alberto Denis-Miranda, Rafael Hernández-Palacio, Mailyn Moreno-Espino, Milton García-Borroto   +4 more
openaire   +1 more source

Use of forest structure to improve classification

2014 IEEE Geoscience and Remote Sensing Symposium, 2014
This paper deals with forest classification in tropical and subtropical areas using multi-sources data fusion. Topological, environmental, structural and visual information are used to classify the samples. This study improves a previous classification by introducing airborne LiDAR information through the computation of the Digital Vegetation Elevation
openaire   +2 more sources

Illuminant classification based on random forest

2015 14th IAPR International Conference on Machine Vision Applications (MVA), 2015
We present a novel machine learning/pattern recognition based colour constancy method. We cast colour constancy as an illumination source recognition problem, and have developed an effective and efficient random forest based classification technique for inferring the class of illumination source of an image.
Bozhi Liu, Guoping Qiu
openaire   +1 more source