Punjabi Text Classification is the process of assigning predefined classes to the unlabelled text documents. Because of dramatic increase in the amount of content available in digital form, text classification becomes an urgent need to manage the digital data efficiently and accurately. Till now no Punjabi Text Classifier is available for Punjabi Text Documents. Therefore, in this paper, existi...

This paper presents a methodology for building an application that can classify healthcare information. It extracts informative sentences from medical papers that mentions about diseases and treatments, and then recognizes semantic relations that exist between the entities in the informative sentences. Support Vector Machine algorithm with String Kernel is used for relation identification. The ...

Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely cont...

This study provides operational guidance for using naïve Bayes Bayesian network (BN) models in bankruptcy prediction. First, we suggest a heuristic method that guides the selection of bankruptcy predictors from a pool of potential variables. The method is based upon the assumption that the joint distribution of the variables is multivariate normal. Variables are selected based upon correlations...

Digital images are now the basis of visual information in medical applications. The advent of radiology which employs imaging for diagnosis generates great amount of images. Automatic retrieval of images based on features like color, shape and texture is termed Content Based Image Retrieval. The increasing dependence of modern medicine on diagnostic techniques such as radiology, computerized to...

Classification, particularly Text Classification, is a supervised learning approach categorizing into various categories, the available training set of correctly identified observations analyzed into a set of features. There are many phases involved in classification. The main classification phase involves the use of classification algorithms or classifiers. Among the various classifiers, the N...

Voting methods such as boosting and bagging provide substantial improvements in classification performance in many problem domains. However, the resulting predictions can prove inscrutable to end-users. This is especially problematic in domains such as medicine, where end-user acceptance often depends on the ability of a classifier to explain its reasoning. Here we propose a variant of the boos...

The supervised classification also known as pattern recognition, discrimination, or supervised learning consists of assigning new cases to one of a set of pre-defined classes given a sample of cases for which the true classes are known. The Naïve Bayes (NB) technique of supervised classification has become increasingly popular in the recent years. Despite its unrealistic assumption that feature...

This research proposes a method to optimize the accuracy of Naïve Bayes (NB) model by optimizing weight using genetic algorithm (GA). The process giving optimal is carried out when data will be input into analysis NB. stages were conducted preprocessing data, searching for classic naïve model, weight, applying hybrid and as final stage, evaluating model. results showed an increase in proposed w...

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...