22,727 research outputs found
Modeling Stroke Diagnosis with the Use of Intelligent Techniques
The purpose of this work is to test the efficiency of specific intelligent classification algorithms when dealing with the domain of stroke medical diagnosis. The dataset consists of patient records of the âAcute Stroke Unitâ, Alexandra Hospital, Athens, Greece, describing patients suffering one of 5 different stroke types diagnosed by 127 diagnostic attributes / symptoms collected during the first hours of the emergency stroke situation as well as during the hospitalization and recovery phase of the patients. Prior to the application of the intelligent classifier the dimensionality of the dataset is further reduced using a variety of classic and state of the art dimensionality reductions techniques so as to capture the intrinsic dimensionality of the data. The results obtained indicate that the proposed methodology achieves prediction accuracy levels that are comparable to those obtained by intelligent classifiers trained on the original feature space
Supervised Classification: Quite a Brief Overview
The original problem of supervised classification considers the task of
automatically assigning objects to their respective classes on the basis of
numerical measurements derived from these objects. Classifiers are the tools
that implement the actual functional mapping from these measurements---also
called features or inputs---to the so-called class label---or output. The
fields of pattern recognition and machine learning study ways of constructing
such classifiers. The main idea behind supervised methods is that of learning
from examples: given a number of example input-output relations, to what extent
can the general mapping be learned that takes any new and unseen feature vector
to its correct class? This chapter provides a basic introduction to the
underlying ideas of how to come to a supervised classification problem. In
addition, it provides an overview of some specific classification techniques,
delves into the issues of object representation and classifier evaluation, and
(very) briefly covers some variations on the basic supervised classification
task that may also be of interest to the practitioner
Classification
In Classification learning, an algorithm is presented with a set of classified examples or ââinstancesââ from which it is expected to infer a way of classifying unseen instances into one of several ââclassesââ. Instances have a set of features or ââattributesââ whose values define that particular instance. Numeric prediction, or ââregression,ââ is a variant of classification learning in which the class attribute is numeric rather than categorical. Classification learning is sometimes called supervised because the method operates under supervision by being provided with the actual outcome for each of the training instances. This contrasts with Data clustering (see entry Data Clustering), where the classes are not given, and with Association learning (see entry Association Learning), which seeks any association â not just one that predicts the class
A traffic classification method using machine learning algorithm
Applying concepts of attack investigation in IT industry, this idea has been developed to design
a Traffic Classification Method using Data Mining techniques at the intersection of Machine
Learning Algorithm, Which will classify the normal and malicious traffic. This classification will
help to learn about the unknown attacks faced by IT industry. The notion of traffic classification
is not a new concept; plenty of work has been done to classify the network traffic for
heterogeneous application nowadays. Existing techniques such as (payload based, port based
and statistical based) have their own pros and cons which will be discussed in this
literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now
Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art
Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover
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