support vector decision tree
Data Visualization and Improving Accuracy of Attrition Using Stacked Classifier
... The three algorithms that are stacked into the stacking classifier are Adaptive Boosting (Adaboost), Decision Tree Classifier (DTC) and Support Vector Machine (SVM). The stacking classifier is ...
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CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study
... reproducibility evaluation and wrapper-based feature se- lection as well as model establishment, was used to minimize the risk of modeling bias and over-fitting as reported by Paul et al. [13]. With these processes, the ...
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Machine Learning Classification Algorithms for Predictive Analysis in Healthcare
... applications. Decision Tree and Support Vector Machine are the machine learning classification algorithm used by the majority of researchers in their heathcare predictive research and are the ...
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Diagnosis Of Learning Disabilities In School Going Children Using Data Mining Techniques: A Survey
... like Support Vector Machine, Neural Network and Decision ...recognition. Decision tree are powerful and popular tool for classification and prediction ...
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Dynamical System of Tuberculosis Considering Lost Sight Compartment
... Breast cancer starts to grow in the human body when cells in the breast are growing most in an unexpected manner. After these cells grow, it can be seen by x- ray. Basically, there are two types of breast cancer, cancer ...
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Classification Techniques for Predicting Graduate Employability
... uncertain. Decision tree, Random Forest, Naïve Bayes, Support Vector Machine (SVM), Artificial Neural Network (ANN) and many other algorithms can be used in classification modeling ...
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IJCSMC, Vol. 5, Issue. 5, May 2016, pg.483 – 488 A Survey on Classification Techniques in Data Mining for Analyzing Liver Disease Disorder
... The study surveyed some data mining techniques to predict the liver disease at earlier stage. The study analyzed algorithms such as C4.5, Naive Bayes, Decision Tree, Support Vector Machine, ...
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ABSTRACT: Word sense disambiguation is solved with the help of various data mining approaches like Naïve Bayes
... Approach, Decision List, decision tree, and SVM (Support Vector ...study Decision List achieved the best result among all other ...
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A Study of Image Classification using Machine learning-A Systematic Approach Aswathythankachan, Bino Thomas Abstract PDF IJIRMET1604010013
... Abstract : Machine learning is an application of artificial intelligence that make the computer to learn by themselves without being explicitly programmed. There are different classification techniques. like supervised ...
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Detect Theft Using Machine Learning In Smart Grid
... due to electrical theft, have been a major concern in power system industries. Large scale consumption of electricity in a fraudulent manner may imbalance the demand supply gap to a great extent. So, keeping focus on ...
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Comparative Study for Text Document Classification Using Different Machine Learning Algorithms
... Classification is a supervised learning method: the goal is finding the labels of the unknown object. In the real world, the tedious amounts of manual works are required to label the unknown documents. The system is ...
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Crime Patterns and Prediction: A Data Mining and Machine Learning Approach
... Multiple learning algorithms have been applied so as to arrive at an output with the highest accuracy possible. The dataset is trained differently for every algorithm. We plot different time-series graphs for our dataset ...
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Comparison Of Datamining Techniques For Prediction Of Breast Cancer
... Data Mining is predicted to be “one of the revolutionary developments of the next decade” [9]. It is the process of discovering interesting patterns and knowledge from large amounts of data [11]. Naive Bayes, Logistic ...
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Comparative Study On Effort Estimation Using Different Data Mining Techniques
... Regression Tree, Regression Analysis, Decision Tree, Random Forest, Logistic Regression, Naive Bayes, K- Nearest Neighbor, Case-based reasoning, Support Vector Machine and Fuzzy Logic ...
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Performance Evaluation of Machine Learning Approaches for Credit Scoring
... and Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGboost), Multi-Layer Perceptron ...
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Improved Intrusion Detection System using C4.5 Decision Tree and Support Vector Machine
... selects support vector along the surface of this function. These support vectors are used by SVM to classify data that outline the hyper plane in the feature ...
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Technical Methods and Algorithms for Developing Efficient Optical Character Recognition System: An Overview
... documents in Optical Character Recognition has been the target of research in the area of pattern recognition. Study in this aspect has been controlled by a need to join the natural process of image input with the data ...
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Predicting Student’s Performance Using Data Mining Techniques: A Survey From 2002 To 2020
... Network, Decision tree, Naïve Bayes, K-Nearest Neighbor and Support Vector Machine are the classification methods used for prediction of student ...used Decision tree and Neural ...
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The Best Separating Decision Tree Twin Support Vector Machine for Multi-Class Classification
... Following the principle of (i) and (ii), we can build our binary tree. Then, following the principle of the (i), we introduce an easy algorithm to build the binary tree by finding the maximum distance ...
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Quantitative Analysis of Ras Oncogene Isoforms Using Decision Tree and Support Vector Machine
... multi-class support vector machine (SVMmulticlass) was used, which uses the multi-class formation ...of support vectors were the lowest with RBF kernel, compared to those with normal and polynomial ...
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