4,329 research outputs found
Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers
In this paper, an issue of building the RRC model using probability
distributions other than beta distribution is addressed. More precisely, in
this paper, we propose to build the RRR model using the truncated normal
distribution. Heuristic procedures for expected value and the variance of the
truncated-normal distribution are also proposed. The proposed approach is
tested using SCM-based model for testing the consequences of applying the
truncated normal distribution in the RRC model. The experimental evaluation is
performed using four different base classifiers and seven quality measures. The
results showed that the proposed approach is comparable to the RRC model built
using beta distribution. What is more, for some base classifiers, the
truncated-normal-based SCM algorithm turned out to be better at discovering
objects coming from minority classes.Comment: arXiv admin note: text overlap with arXiv:1901.0882
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
Machine Learning Algorithms in Analysis, Diagnosing and Predicting COVID-19: A Systematic Literature Review
Since the COVID-19 corona virus first appeared at the end of 2019, in Wuhan province, China, the analysis, diagnosis, and prognosis of COVID-19 (SARS-CoV-2) has attracted the greatest attention. Since then, every part of the world needs some sort of system or instrument to assist judgments for prompt quarantine and medical treatment. For a variety of uses, including prediction, classification, and analysis, machine learning (MLR) have demonstrated their accuracy and efficiency in the fields of education, health, and security. In this paper, three main questions will be answered related to COVID-19 analysis, predicting, and diagnosing. The performance evaluation, fast process and identification, quick learning, and accurate results of MLR algorithms make them as a base for all models in analyzing, diagnosing, and predicting COVID-19 infection. The impact of using supervised and unsupervised MLR can be used for estimating the spread level of COVID-19 to make the proper strategic decisions. The researchers next compared the effects of various datatypes on diagnosing, forecasting, and assessing the severity of COVID-19 infection in order to examine the effects of MLRs. Three fields are associated with COVID-19, according to the analysis of the chosen study (analysis, diagnosing, and predicting). The majority of researches focus on the subject of COVID-19 diagnosis, where they use their models to identify the infection. In the selected studies, several algorithms are employed, however, a study revealed that the neural network is the most used method when compared to other algorithms. The most used method for identifying, forecasting, and evaluating COVID-19 infection is supervised MLR
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