scholarly journals Automated Detection of Connective Tissue by Tissue Counter Analysis and Classification and Regression Trees

2001 ◽  
Vol 23 (3-4) ◽  
pp. 153-158 ◽  
Author(s):  
Josef Smolle ◽  
Peter Kahofer
Keyword(s):  
Connective Tissue ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Data Sets ◽  
Classification Rules ◽  
Normal Human Skin ◽  
Reticular Dermis ◽  
Independent Test ◽  
Normal Human ◽  
Classification And Regression

Objective: To evaluate the feasibility of the CART (Classification and Regression Tree) procedure for the recognition of microscopic structures in tissue counter analysis. Methods: Digital microscopic images of H&E stained slides of normal human skin and of primary malignant melanoma were overlayed with regularly distributed square measuring masks (elements) and grey value, texture and colour features within each mask were recorded. In the learning set, elements were interactively labeled as representing either connective tissue of the reticular dermis, other tissue components or background. Subsequently, CART models were based on these data sets.Results: Implementation of the CART classification rules into the image analysis program showed that in an independent test set 94.1% of elements classified as connective tissue of the reticular dermis were correctly labeled. Automated measurements of the total amount of tissue and of the amount of connective tissue within a slide showed high reproducibility (r=0.97 andr=0.94, respectively; p < 0.001).Conclusions: CART procedure in tissue counter analysis yields simple and reproducible classification rules for tissue elements.

scholarly journals SELECTING KDD FEATURES AND USING RANDOM CLASSIFICATION TREE FOR PREDICTING ATTACKS

2014 ◽  
pp. 115-123
Author(s):  
Rachid Beghdad
Keyword(s):  
Machine Learning ◽  
Classification Tree ◽  
Regression Tree ◽  
Classification Trees ◽  
Classification And Regression Tree ◽  
Data Sets ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Classification And Regression ◽  
Anova Technique

The purpose of this study is to identify some higher-level KDD features, and to train the resulting set with an appropriate machine learning technique, in order to classify and predict attacks. To achieve that, a two-steps approach is proposed. Firstly, the Fisher’s ANOVA technique was used to deduce the important features. Secondly, 4 types of classification trees: ID3, C4.5, classification and regression tree (CART), and random tree (RnDT), were tested to classify and detect attacks. According to our tests, the RndT leads to the better results. That is why we will present here the classification and prediction results of this technique in details. Some of the remaining results will be used later to make comparisons. We used the KDD’99 data sets to evaluate the considered algorithms. For these evaluations, only the four attack categories’ case was considered. Our simulations show the efficiency of our approach, and show also that it is very competitive with some similar previous works.

Classification and regression tree-based prediction of 6-mercaptopurine-induced leucopenia grades in children with acute lymphoblastic leukemia

2019 ◽  
Vol 83 (5) ◽  
pp. 875-880 ◽  
Author(s):  
Shaik Mohammad Naushad ◽  
Patchava Dorababu ◽  
Yedluri Rupasree ◽  
Addepalli Pavani ◽  
Digumarti Raghunadharao ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Classification And Regression
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