Gini-Impurity Index Analysis

Yuan, Ye; Wu, Liji; Zhang, Xiangmin

doi:10.1109/tifs.2021.3076932

Cited by 54 publications

(21 citation statements)

References 33 publications

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“…The winning feature is chosen in a deterministic manner to maximize the homogeneity in each child node. We use Gini impurity 30 criteria to choose the winner feature and also as a measure of homogeneity in our design. The homogeneity of a node is calculated based on the distribution of data with different class labels in that node.…”

Section: Methodsmentioning

confidence: 99%

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Differential diagnosis of thyroid nodule capsules using random forest guided selection of image features

Eftimie

Glogojeanu

Tejaswee

et al. 2022

Sci Rep

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Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital pathology is gaining momentum providing the pathologist with additional cues to traditional routes when placing a diagnosis, therefore it is extremely important to develop new image analysis methods that can extract image features with diagnostic potential. In this work, we use histogram and texture analysis to extract features from microscopic images acquired on thin thyroid nodule capsules sections and demonstrate how they enable the differential diagnosis of thyroid nodules. Targeted thyroid nodules are benign (i.e., follicular adenoma) and malignant (i.e., papillary thyroid carcinoma and its sub-type arising within a follicular adenoma). Our results show that the considered image features can enable the quantitative characterization of the collagen capsule surrounding thyroid nodules and provide an accurate classification of the latter’s type using random forest.

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Section: Methodsmentioning

confidence: 99%

“…A feature that causes more reduction of information across the trees of a random forest is assigned more importance in our computation. This is called Gini importance 30 . Based on the importance values, we take the top N most important features for the subsequent classification task.…”

Section: Methodsmentioning

confidence: 99%

Differential diagnosis of thyroid nodule capsules using random forest guided selection of image features

Eftimie

Glogojeanu

Tejaswee

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The Gini index is selected as the splitting attribute, and finally the binary tree is generated. 18 Gini coefficient is used to represent the impurity of data set. To represent sample set H , its Gini coefficient can be expressed as follows: where Pi represents the probability that the data in the sample set H belongs to class n .…”

Section: The Review Of Research Methodsmentioning

confidence: 99%

Study on the university students' satisfaction of the wisdom tree massive open online course platform based on parameter optimization intelligent algorithm

et al. 2021

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Introduction: Curriculum learning through the wisdom tree massive open online course platform not only gets rid of the limitations of specialty, school and region, eliminates the limitations of time and space in traditional teaching, but also effectively solves the problem of educational equity. Objectives: This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. Methods: This study takes the university students in Fuzhou city information management department as the survey object, and adopts the electronic questionnaire survey method. A total of 1136 formal questionnaires were responded, and 1028 valid questionnaires were obtained after data cleaning and deleting invalid questionnaires (the effective rate was 90.49%). In this paper, the reliability and validity of the questionnaire were tested by IBM SPSS-20.0 software, and six explanatory variables including function, achievement, exercise, quality, richness, and interaction were obtained by principal component analysis. Then, the questionnaire data is converted to CSV (comma separated values) format for analysis. This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. In this paper, the proposed algorithm is compared with decision tree, random forest, k-nearest neighbor, and support vector machine to verify its performance. Results: The experimental results show that training set classification accuracy of decision tree, random forest, k-nearest neighbor, only support vector machine and the proposed algorithm (simulated annealing + support vector machine) are 92.21%, 96.10%, 95.67%, 97.29%, and 99.58%, respectively. Conclusion: The proposed algorithm simulated annealing + support vector machine does increase the classification accuracy. At the same time, the 11 decision rules generated by simulated annealing + decision tree can provide useful information for decision makers.

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“…In a general way, the best separation is the one that divides the data into groups such that there is a dominant class. To measure that, the algorithm in our experiments uses the Gini diversity index, which is one of the possible impurity measures [40]. The Gini diversity index is a measure of how often a randomly chosen item from a set would be incorrectly labelled if it were randomly labelled according to the distribution of labels in the subset.…”

Section: Machine Learning Modelmentioning

confidence: 99%

A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults

Martí-Puig

Capra

Vega

et al. 2022

Sensors

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Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively.

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Gini-Impurity Index Analysis

Cited by 54 publications

References 33 publications

Differential diagnosis of thyroid nodule capsules using random forest guided selection of image features

Differential diagnosis of thyroid nodule capsules using random forest guided selection of image features

Study on the university students' satisfaction of the wisdom tree massive open online course platform based on parameter optimization intelligent algorithm

A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults

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