Improving the Readability of Decision Trees Using Reduced Complexity Feature Extraction

2005 ◽  
pp. 442-444
Author(s):  
Cesar Fernandez ◽  
Sampsa Laine ◽  
Oscar Reinoso ◽  
M. Asuncion Vicente
Keyword(s):  
Feature Extraction ◽  
Decision Trees ◽  
Reduced Complexity

scholarly journals Probabilistic Ensemble of Deep Information Networks

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 100 ◽  
Author(s):  
Giulio Franzese ◽  
Monica Visintin
Keyword(s):  
Information Theory ◽  
Cost Function ◽  
Decision Trees ◽  
Information Networks ◽  
Information Bottleneck ◽  
Tree Classifier ◽  
Reduced Complexity ◽  
Function Information

We describe a classifier made of an ensemble of decision trees, designed using information theory concepts. In contrast to algorithms C4.5 or ID3, the tree is built from the leaves instead of the root. Each tree is made of nodes trained independently of the others, to minimize a local cost function (information bottleneck). The trained tree outputs the estimated probabilities of the classes given the input datum, and the outputs of many trees are combined to decide the class. We show that the system is able to provide results comparable to those of the tree classifier in terms of accuracy, while it shows many advantages in terms of modularity, reduced complexity, and memory requirements.

On the Use of Evolutionary Algorithms in Data Mining

Data Mining ◽  
2011 ◽  
pp. 22-46 ◽  
Author(s):  
Erick Cantu-Paz
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Feature Extraction ◽  
Feature Selection ◽  
Evolutionary Algorithms ◽  
Decision Trees ◽  
Raw Data ◽  
Open Research ◽  
Disciplinary Field ◽  
Research Problems

With computers becoming more pervasive, disks becoming cheaper, and sensors becoming ubiquitous, we are collecting data at an ever-increasing pace. However, it is far easier to collect the data than to extract useful information from it. Sophisticated techniques, such as those developed in the multi-disciplinary field of data mining, are increasingly being applied to the analysis of these datasets in commercial and scientific domains. As the problems become larger and more complex, researchers are turning to heuristic techniques to complement existing approaches. This survey chapter examines the role that evolutionary algorithms (EAs) can play in various stages of data mining. We consider data mining as the end-to-end process of finding patterns starting with raw data. The chapter focuses on the topics of feature extraction, feature selection, classification, and clustering, and surveys the state of the art in the application of evolutionary algorithms to these areas. We examine the use of evolutionary algorithms both in isolation and in combination with other algorithms including neural networks, and decision trees. The chapter concludes with a summary of open research problems and opportunities for the future.

Interpretable Model for Artefact Detection in Local Field Potentials via Feature Extraction and Decision Trees

2021 ◽  
pp. 288-299
Author(s):  
Marcos I. Fabietti ◽  
Mufti Mahmud ◽  
Ahmad Lotfi ◽  
Alberto Averna ◽  
David Guggenmos ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Decision Trees ◽  
Local Field ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Artefact Detection ◽  
Interpretable Model

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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