Classification of heart disease using multiple classifiers
نویسندگان
چکیده
منابع مشابه
Question Classification using Multiple Classifiers
The Open-domain Question Answering system (QA) has been attached great attention for its capacity of providing compact and precise results for sers. The question classification is an essential part in the system, affecting the accuracy of it. The paper studies question classification through machine learning approaches, namely, different classifiers and multiple classifier combination method. B...
متن کاملImageNet Classification with Multiple Classifiers
In this project we proposed an ensemble classifier to classify over 20 thousand images sampled from ImageNet, which originally has over 10 million images. One of the challenge of this classification problem is that the images cannot be precisely represented by one type of features, such as SIFT and GIST. Hence, in this project, we use different kinds of features. Another challenge is that diffe...
متن کاملAdversarial Pattern Classification Using Multiple Classifiers and Randomisation
In many security applications a pattern recognition system faces an adversarial classification problem, in which an intelligent, adaptive adversary modifies patterns to evade the classifier. Several strategies have been recently proposed to make a classifier harder to evade, but they are based only on qualitative and intuitive arguments. In this work, we consider a strategy consisting in hiding...
متن کاملPrediction of Heart Disease using Classification Algorithms
Data mining is an iterative progress in which evolution is defined by detection, through usual or manual methods. The discovered knowledge can be used for different applications for example healthcare industry. The heart disease accounts to be the leading cause of death worldwide. It is difficult for medical practitioners to predict the heart attack as it is complex task that requires experienc...
متن کاملCollective Classification Using Heterogeneous Classifiers
Collective classification algorithms have been used to improve classification performance when network training data with content, link and label information and test data with content and link information are available. Collective classification algorithms use a base classifier which is trained on training content and link data. The base classifier inputs usually consist of the content vector ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Review of Computer Engineering Studies
سال: 2019
ISSN: 2369-0755,2369-0763
DOI: 10.18280/rces.050301