Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble
نویسندگان
چکیده
منابع مشابه
Dynamic Cost-sensitive Naive Bayes Classification for Uncertain Data
The uncertain data as an important aspect of data mining, has received considerable attention, due to its importance in many applications, but little study has been paid to the cost-sensitive classification on uncertain data, so this paper proposes the dynamic costsensitive Naive Bayes classification for mining uncertain data (DCSUNB). Firstly, we apply the probability density to dispose uncert...
متن کاملA New Approach for Text Documents Classification with Invasive Weed Optimization and Naive Bayes Classifier
With the fast increase of the documents, using Text Document Classification (TDC) methods has become a crucial matter. This paper presented a hybrid model of Invasive Weed Optimization (IWO) and Naive Bayes (NB) classifier (IWO-NB) for Feature Selection (FS) in order to reduce the big size of features space in TDC. TDC includes different actions such as text processing, feature extraction, form...
متن کاملNaive-Bayes for Sentiment Classification
This report details the findings in building a naive Bayes sentiment classifier for a IMDB movie-review data set using Scala and ScalaNLP. We studied the unigram or bagof-words Bernoulli and Multinomial models and a number of different feature selection techniques, including term frequency, mutual information and Chi-squared. 1. DATA CORPUS The corpus contains of 2000 rated movie reviews, compr...
متن کاملADABOOST ENSEMBLE ALGORITHMS FOR BREAST CANCER CLASSIFICATION
With an advance in technologies, different tumor features have been collected for Breast Cancer (BC) diagnosis, processing of dealing with large data set suffers some challenges which include high storage capacity and time require for accessing and processing. The objective of this paper is to classify BC based on the extracted tumor features. To extract useful information and diagnose the tumo...
متن کاملCost-sensitive Naïve Bayes Classification of Uncertain Data
Data uncertainty is widespread in real-word applications. It has captured a lot of attention, but little job has been paid to the research of cost sensitive algorithm on uncertain data. The paper proposes a novel cost-sensitive Naïve Bayes algorithm CS-DTU for classifying and predicting uncertain datasets. In the paper, we apply probability and statistics theory on uncertain data model, define ...
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: Computational and Mathematical Methods in Medicine
سال: 2021
ISSN: 1748-6718,1748-670X
DOI: 10.1155/2021/5556992