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...

Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noisefree data while some of them are better than others on noisy data. But in reality, ensemble methods that can consistently gain good performance in situations with or without noise are more desirable. In this paper, we pr...

Output Coding is a method of converting a multiclass problem into several binary subproblems and gives an ensemble of binary classifiers. Like other ensemble methods, its performance depends on the accuracy and diversity of base classifiers. If a decision tree is chosen as base classifier, the issue of tree pruning needs to be addressed. In this paper we investigate the effect of six methods of...

Cardiovascular diseases are a cluster of heart-related issues, including many comorbidities, which becoming leading cause human death across the globe. Hence, an essential framework is demanded for early detection CVDs can help to prevent premature death. The application Artificial Intelligence (AI) in healthcare has opted this challenge and makes it easier detect using computational model. In ...

Abstract Using differential privacy to provide protection for classification algorithms has become a research hotspot in data mining. In this paper, we analyze the defects differentially private decision tree named Maxtree, and propose an improved model DPtree. DPtree can use Fayyad theorem process continuous features quickly, adjust budget adaptively according sample category distributions lea...

Ensemble methods have become very well known for being powerful pattern recognition algorithms capable of achieving high accuracy. However, Ensemble methods produces learners that are not comprehensible or transferable thus making them unsuitable for tasks that require a rational justification for making a decision. Rule Extraction methods can resolve this limitation by extracting comprehensibl...

In this paper, we propose an ensemble method based on bagging and decision tree to resolve the problem of diagnosing out-of-control signals in multivariate statistical process control. To classify the out-of-control signals, we obtain a series of classifiers through ensemble learning on decision tree. Then we will integrate the classification results of multiple classifiers to determine the fin...