Lazy learning algorithms, exemplified by nearestneighbor algorithms, do not induce a concise hypothesis from a given training set; the inductive process is delayed until a test instance is given. Algorithms for constructing decision trees, such as C4.5, ID3, and CART create a single “best” decision tree during the training phase, and this tree is then used to classify test instances. The tests ...

Introduction: Attention-Deficit/Hyperactivity Disorder (ADHD) is a well-known neurodevelopmental disorder. Diagnosis and treatment of ADHD can often lead to a developmental trajectory toward positive results. The present study aimed at implementing the decision tree method to recognize children with and without ADHD, as well as ADHD subtypes.  Methods: In the present study, the subjects includ...

Academic dishonesty becomes an exciting phenomenon to be examined. This research aimed examine the effect of subjective norms on academic dishonesty. Data were collected from 426 accounting students public and private universities in Yogyakarta, Indonesia. The data analysed with J48 algorithm decision tree. interest that happened low node was divided into universities. Based tree visualization,...

Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and model interpretability. With the emergence of big data, there is an increasing need to parallelize the training process of decision tree. However, most existing attempts along this line suffer from high communication co...

A non-greedy approach for constructing globally optimal multivariate decision trees with xed structure is proposed. Previous greedy tree construction algorithms are locally optimal in that they optimize some splitting criterion at each decision node, typically one node at a time. In contrast, global tree optimization explicitly considers all decisions in the tree concurrently. An iterative line...

Decision trees have been found very effective for classification especially in Data Mining. This paper aims at improving the performance of the SLIQ decision tree algorithm (Mehta et. al,1996) for classification in data mining The drawback of this algorithm is that large number of gini indices have to be computed at each node of the decision tree. In order to decide which attribute is to be spl...

Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new techn...

The purpose of data classification is to construct a classification model. The decision tree algorithm is a more general data classification function approximation algorithm based on machine learning. The decision tree is directed and acyclic. Iterative Dichotomiser 3(ID3) algorithm invented by Ross Quinlan is used to generate decision tree from a dataset. Considering its limitations layer an o...

Kajal Rai Research Scholar, Department of Computer Science and Applications, Panjab University, Chandigarh, India Email: [email protected] M. Syamala Devi Professor, Department of Computer Science and Applications, Panjab University, Chandigarh, India Email: [email protected] Ajay Guleria System Manager, Computer Center, Panjab University, Chandigarh, India Email: [email protected] -------------------...