Entropy and Algorithm of the Decision Tree for Approximated Natural Intelligence

Popova, Olga; Shevtsov, Yury; Popov, Boris; Karandey, Vladimir; Klyuchko, Vladimir; Gerashchenko, Alexander

doi:10.1007/978-3-319-94229-2_30

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…They are: i) Gini impurity and ii) entropy. Steps involved in analyzing the features using decision tree Entropy [13] is a term used in the decision tree for splitting trees into smaller subsets. Entropy was used to identify the best feature in the given datasets.…”

Section: Decision Tree Analysismentioning

confidence: 99%

Feature based analysis of endometriosis using machine learning

Visalaxi

SudalaiMuthu²,

Ramalingam³

2023

IJEECS

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<span lang="EN-US">Machine learning is a cutting-edge technology used for predicting and diagnosing various diseases. Various machine learning algorithm facilitates the prediction. The decision tree belongs to learning algorithm that performs both classification and prediction. The decision tree constructs the tree-like to evaluate the best features. The decision tree performs well in the prediction of various diseases. Endometriosis is a recurrence disease that creates an emotional impact in women. Endometriosis is a lump-like structure that appears at several locations in reproductive organs of women. The diagnosis of endometriosis was predicted through scanning procedures and laparoscopic procedures. The symptoms identified from laparoscopic surgery were used as the features for predicting the severity of endometriosis. The symptoms include mass-like structure, tissue size, variation in tissue colour, and blockages in fallopian tubes. The decision tree analyze the features of endometriosis by using two criteria such as entropy and Gini index. The entropy and Gini index construct the tree by identifying the size of tissue as major influencing attributes. The Gini index outperforms well with training accuracy of 84.08% and test accuracy of 84.85.</span>

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Section: Decision Tree Analysismentioning

confidence: 99%

Feature based analysis of endometriosis using machine learning

Visalaxi

SudalaiMuthu²,

Ramalingam³

2023

IJEECS

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“…В результате у конструктора может не оказаться нужного опыта и математических знаний. Для решения этой проблемы необходимо создавать новые алгоритмы [30][31] для создания специальных информационных помощников [32][33]…”

Section: Introductionunclassified

Application of the Geometric Programming Method to Study the Optimal Parameters of Special Electric Drives

Karandey

Popov

Popova

et al. 2022

Gorn. oborud. èlektromeh.

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Research of Methods of Optimal Design of Special Electric Drives

Karandey

Popov

Popova

et al. 2021

J. Phys.: Conf. Ser.

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Problems of optimization of special electrical and electromechanical systems in modeling, creation and design are solved mainly by methods of mathematical programming. The task of mathematical programming is to find extremes of the function of many variables in the presence of restrictions on variables, which creates fundamental difficulties. To solve such problems, the number of methods for solving the general problem of mathematical programming is currently expanding significantly. In this regard, the trend in the development of mathematical programming is following the path of highlighting and studying various subclasses of problems. The use of certain specific features of the tasks of the selected subclass creates opportunities for their more effective investigation and solution. Examples of such subclasses give convex, quadratic, linear programming problems, transport-type problems, and others. Geometric programming is also a section of mathematical programming that research a certain class of optimization problems. However, when using geometric programming, you have to apply linear, nonlinear programming, the concept of convex programming. Using optimization methods will allow you to correctly investigate, design and create special electric drives.

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Entropy and Algorithm of the Decision Tree for Approximated Natural Intelligence

Cited by 4 publications

References 7 publications

Feature based analysis of endometriosis using machine learning

Feature based analysis of endometriosis using machine learning

Application of the Geometric Programming Method to Study the Optimal Parameters of Special Electric Drives

Research of Methods of Optimal Design of Special Electric Drives

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