Selecting Optimal Split-Functions for Large Datasets

Stoffel, Kilian; Raileanu, Laura Elena

doi:10.1007/978-1-4471-0269-4_5

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…The training set was composed of 70% of all the input data and the test with the remaining 30%. Gain ration and Gini index were used to rank all features according to their correlation with each class [17,18]. Thus, we selected the five features that achieved the highest scores for classification within each machine learning classifier and each operator.…”

Section: Machine Learning Classificationmentioning

confidence: 99%

Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients

et al. 2021

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Evaluate whether texture analysis associated with machine learning approaches could differentiate between malignant and benign lymph nodes. A total 18 patients with lung cancer were selected, with 39 lymph nodes, being 15 malignant and 24 benign. Retrospective computed tomography scans were utilized both with and without contrast medium. The great differential of this work was the use of 15 textures from mediastinal lymph nodes, with five different physicians as operators. First and second order statistical textures such as gray level run length and co-occurrence matrix were extracted and applied to three different machine learning classifiers. The best machine learning classifier demonstrated a variability of less than 5% among operators. The support vector machine (SVM) classifier presented 95% of the area under the ROC curve (AUC) and 89% of sensitivity for sequences without contrast medium. SVM classifier presented 93% of AUC and 86% of sensitivity for sequences with contrast medium. Texture analysis and machine learning may be helpful in the differentiation between malign and benign lymph nodes. This study can aid the physician in diagnosis and staging of lymph nodes and potentially reduce the number of invasive analysis to histopathological confirmation.

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Section: Machine Learning Classificationmentioning

confidence: 99%

Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients

et al. 2021

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“…Two widely used split criterion is Gini Index and Information Gain. A lot of research was dedicated to understand which of them produce the best decision tree for a given dataset [46], [38], [70], [55]. Although most of empirical studies concluded that there is no significant differences between those two criteria and the disagreement is generally no higher than 2% of all cases, we show the validation accuracy achieved with different feature sets when using different criteria with Extra-Trees classifier (n estimators = 700) in Fig.…”

Section: H Parameter Tuning In Extra-trees Classifiermentioning

confidence: 99%

Fingerprinting Search Keywords over HTTPS at Scale

Yan,

Alan,

Kaur

2020

Preprint

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The possibility of fingerprinting the search keywords issued by a user on popular web search engines is a significant threat to user privacy. This threat has received surprisingly little attention in the network traffic analysis literature. In this work, we consider the problem of keyword fingerprinting of HTTPS traffic-we study the impact of several factors, including client platform diversity, choice of search engine, feature sets as well as classification frameworks. We conduct both closedworld and open-world evaluations using nearly 4 million search queries collected over a period of three months. Our analysis reveals several insights into the threat of keyword fingerprinting in modern HTTPS traffic.

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