A DT is a classification scheme which generates a tree and a set of rules, representing the model of different classes, from a given dataset. As per Hans and Kamber [HK01], DT is a flow chart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test and leaf nodes represent the classes or class distributions. The top most node in...

As the early diagnosis of autism spectrum disorder (ASD) is critical high accuracy machine learning can be applied to achieve technology based diagnosis. In this context, present study demonstrates approach for ASD using decision tree (DT) modeling. The dataset employed in comprises two classes adults with a sample size 704 instances. DT model entails recursive partitioning implemented “rpart” ...

The present study aimed to develop a methodology estimate jute (Corchorus sp.) cultivated areas in the Indian subcontinent using sentinel-1A Synthetic aperture radar (SAR) C band dual polarization data. We developed hierarchical decision tree (DT) model area and compared it with other supervised unsupervised methods. proposed DT showed improved overall accuracy of 82–83% (Kappa coefficient, k =...

Prediction of cancer survivability using machine learning techniques has become a popular approach in recent years. ‎In this regard, an important issue is that preparation of some features may need conducting difficult and costly experiments while these features have less significant impacts on the final decision and can be ignored from the feature set‎. ‎Therefore‎, ‎developing a machine for p...

Kernel methods are considered the most effective techniques for various relation extraction (RE) tasks as they provide higher accuracy than other approaches. In this paper, we introduce new dependency tree (DT) kernels for RE by improving on previously proposed dependency tree structures. These are further enhanced to design more effective approaches that we call mildly extended dependency tree...

Today, kidney stone detection is done manually on medical images. This process time-consuming and subjective as it depends the physician. study aims to classify healthy or patient persons according status of stones from images using various machine learning methods Convolutional Neural Networks (CNNs). We evaluated such Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVC), Mul...

In this work, we developed artificial intelligence-based models for prediction and correlation of CO2 solubility in amino acid solutions the purpose capture. The were used to correlate process parameters loading solvent. Indeed, loading/solubility solvent was considered as sole model’s output. studied work potassium sodium-based salt solutions. For predictions, tried three potential models, inc...

Deep neural networks (DNNs), the integration of (NNs) and deep learning (DL), have proven highly efficient in executing numerous complex tasks, such as data image classification. Because multilayer a nonlinearly separable structure is not transparent, it critical to develop specific classification model from new unexpected dataset. In this paper, we propose novel approach using concepts DNN dec...

In this paper, a new decision tree learning algorithm (INC-DT) is proposed: an incremental one based on hypotheses assessments. INC-DT uses only a xed amount of instance memory during the whole learning process. It bases its hypotheses exclusively on the last one computed , its assessment, and a xed number of seen examples. So, it is able to deal with initial knowledge and concept drift. Additi...

In the classification calculation, data are sometimes not unique and there different values probabilities. Then, it is meaningful to develop appropriate methods make decision. To solve this issue, paper proposes machine learning based on a probabilistic decision tree (DT) under multi-valued preference environment respectively for aims. First, develops pre-processing method deal with weight quan...