Classification accuracy and cut point selection

2012 ◽  
Vol 31 (23) ◽  
pp. 2676-2686 ◽  
Author(s):  
Xinhua Liu
Keyword(s):  
Classification Accuracy ◽  
Point Selection ◽  
Cut Point

scholarly journals An Automated Unsupervised Discretization Method: A Novel Approach

2020 ◽  
Vol 07 (03) ◽  
pp. 301-322
Author(s):  
Habiba Drias ◽  
Hadjer Moulai ◽  
Yassine Drias
Keyword(s):  
Classification Accuracy ◽  
Discretization Method ◽  
Discretization Scheme ◽  
Input Stream ◽  
Cut Point ◽  
Scanning Process ◽  
Novel Approach ◽  
Public Datasets ◽  
The Impact ◽  
First Time

In this paper, for the first time, a novel discretization scheme is proposed aiming at enabling scalability but also at least three other strong challenges. It is based on a Left-to-Right (LR) scanning process, which partitions the input stream into intervals. This task can be implemented by an algorithm or by using a generator that builds automatically the discretization program. We focus especially on unsupervised discretization and design a method called Usupervised Left to Right Discretization (ULR-Discr). Extensive experiments were conducted using various cut-point functions on small, large and medical public datasets. First, ULR-Discr variants under different statistics are compared between themselves with the aim at observing the impact of the cut-point functions on accuracy and runtime. Then the proposed method is compared to traditional and recent techniques for classification. The result is that the classification accuracy is highly improved when using our method for discretization.

Classification Accuracy of Sequentially Administered WAIS-IV Short Forms

2011 ◽  
Author(s):  
David S. Kreiner ◽  
Joseph J. Ryan ◽  
Samuel T. Gontkovsky
Keyword(s):  
Classification Accuracy ◽  
Short Forms

Classification accuracy of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2)-Restructured form validity scales in detecting malingered pain-related disability.

2018 ◽  
Vol 30 (7) ◽  
pp. 857-869 ◽  
Author(s):  
Kevin J. Bianchini ◽  
Luis E. Aguerrevere ◽  
Kelly L. Curtis ◽  
Tresa M. Roebuck-Spencer ◽  
F. Charles Frey ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Minnesota Multiphasic Personality Inventory ◽  
Personality Inventory ◽  
Validity Scales

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

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