Random forests and nearest shrunken centroids for the classification of sensor array data

2008 ◽  
Vol 131 (1) ◽  
pp. 93-99 ◽  
Author(s):  
Matteo Pardo ◽  
Giorgio Sberveglieri
Keyword(s):  
Random Forests ◽  
Sensor Array ◽  
Array Data ◽  
Nearest Shrunken Centroids

scholarly journals Multi-Class Assessment Based on Random Forests

2021 ◽  
Vol 11 (3) ◽  
pp. 92
Author(s):  
Mehdi Berriri ◽  
Sofiane Djema ◽  
Gaëtan Rey ◽  
Christel Dartigues-Pallez
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Random Forests ◽  
Learning Algorithm ◽  
Teaching Staff ◽  
Machine Learning Algorithm ◽  
Process Data ◽  
Training Courses ◽  
Education Courses

Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses adapted to students by anticipating their possible difficulties. This is possible thanks to a machine learning algorithm called Random Forest, allowing for the classification of the students depending on their results. We had to process data, generate models using our algorithm, and cross the results obtained to have a better final prediction. We tested our method on different use cases, from two classes to five classes. These sets of classes represent the different intervals with an average ranging from 0 to 20. Thus, an accuracy of 75% was achieved with a set of five classes and up to 85% for sets of two and three classes.

Automatic Grain Type Classification of Snow Micro Penetrometer Signals With Random Forests

2013 ◽  
Vol 51 (6) ◽  
pp. 3328-3335 ◽  
Author(s):  
Scott Havens ◽  
Hans-Peter Marshall ◽  
Christine Pielmeier ◽  
Kelly Elder
Keyword(s):  
Random Forests ◽  
Type Classification

Parallel Factor Analysis for Gas Sensor Array Signals

2014 ◽  
Vol 494-495 ◽  
pp. 955-959 ◽  
Author(s):  
Wen Na Zhang ◽  
Guo Jun Qin ◽  
Niao Qing Hu
Keyword(s):  
Factor Analysis ◽  
Gas Sensor ◽  
Sensor Array ◽  
Parallel Factor Analysis ◽  
Time Profile ◽  
Three Dimension ◽  
Gas Sensor Array ◽  
Parallel Factor ◽  
Sample Time

Data from sensor array are often arranged in three-dimension as sample × time × sensor. Traditional methods are mainly used for two-dimension data. When such methods are applied, some time-profile information will lost. To acquire the information of samples, sensors and times more exactly, parallel factor analysis (PARAFAC) is investigated to deal with three-way data array. Through the analysis and classification of three kinds of oil odor samples, the performance of PARAFAC in gas sensor array signal analysis is verified and validated.

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