Feature Extraction and Classification of Colon Cancer Using a Hybrid Approach of Supervised and Unsupervised Learning

2021 ◽  
pp. 195-219
Author(s):  
Joydev Ghosh ◽  
Amitesh Kumar Sharma ◽  
Sahil Tomar
Keyword(s):  
Colon Cancer ◽  
Feature Extraction ◽  
Unsupervised Learning ◽  
Hybrid Approach ◽  
Supervised And Unsupervised Learning

Fusion of supervised and unsupervised learning for improved classification of hyperspectral images

2012 ◽  
Vol 217 ◽  
pp. 39-55 ◽  
Author(s):  
Naif Alajlan ◽  
Yakoub Bazi ◽  
Farid Melgani ◽  
Ronald R. Yager
Keyword(s):  
Unsupervised Learning ◽  
Hyperspectral Images ◽  
Supervised And Unsupervised Learning

scholarly journals A study on the early detection of colon cancer using the methods of wavelet feature extraction and SVM classifications of FTIR

2008 ◽  
Vol 22 (5) ◽  
pp. 397-404 ◽  
Author(s):  
Cun-Gui Cheng ◽  
Yu-Mei Tian ◽  
Wen-Ying Jin
Keyword(s):  
Colon Cancer ◽  
Feature Extraction ◽  
Early Detection ◽  
Kernel Functions ◽  
Early Carcinoma ◽  
Support Vector ◽  
Advanced Carcinoma ◽  
Sd Rats ◽  
Rbf Kernel

This paper introduces a new method for the early detection of colon cancer using a combination of feature extraction based on wavelets for Fourier Transform Infrared Spectroscopy (FTIR) and classification using the Support Vector Machine (SVM). The FTIR data collected from 36 normal SD rats, 60 1,2-DMH-induced SD rats, and 44 second generation rats of those induced rats was first preprocessed. Then, 12 feature variants were extracted using continuous wavelet analysis. The extracted feature variants were then inputted into the SVM for classification of normal, dysplasia, early carcinoma, and advanced carcinoma. Among the kernel functions the SVM used, the Poly and RBF kernels had the highest accuracy rates. The accuracy of the Poly kernel in normal, dysplasia, early carcinoma, and advanced carcinoma were 100, 97.5, 95% and 100% respectively. The accuracy of RBF kernel in normal, dysplasia, early carcinoma, and advanced carcinoma was 100, 95, 95% and 100% respectively. The results indicated that this method could effectively and easily diagnose colon cancer in its early stages.

Duality Between Learning Machines: A Bridge Between Supervised and Unsupervised Learning

1994 ◽  
Vol 6 (3) ◽  
pp. 491-508 ◽  
Author(s):  
J.-P. Nadal ◽  
N. Parga
Keyword(s):  
Theoretical Analysis ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Learning Task ◽  
Maximum Information ◽  
Learning Machines ◽  
Learning Tasks ◽  
Supervised And Unsupervised Learning ◽  
Second Dual

We exhibit a duality between two perceptrons that allows us to compare the theoretical analysis of supervised and unsupervised learning tasks. The first perceptron has one output and is asked to learn a classification of p patterns. The second (dual) perceptron has p outputs and is asked to transmit as much information as possible on a distribution of inputs. We show in particular that the maximum information that can be stored in the couplings for the supervised learning task is equal to the maximum information that can be transmitted by the dual perceptron.

scholarly journals Using clustering to aid text classification of single-labelled datasets

2009 ◽  
Author(s):  
Αντωνία Κυριακοπούλου
Keyword(s):  
Unsupervised Learning ◽  
Text Classification ◽  
Data Representation ◽  
Classification Performance ◽  
Critical Research ◽  
Social Bookmarking ◽  
Supervised And Unsupervised Learning ◽  
Text Classifiers ◽  
Concise Representation

Supervised and unsupervised learning have been the focus of critical research in the areas of machine learning and artificial intelligence. In the literature, these two streams flow independently of each other, despite their close conceptual and practical connections. This dissertation demonstrates that unsupervised learning algorithms, i.e. clustering, can provide us with valuable information about the data and help in the creation of high-accuracy text classifiers. In the case of clustering,the aim is to extract a kind of \structure" from a given sample of objects. The reasoning behind this is that if some structure exists in the objects, it is possible to take advantage of this information and find a short description of the data,exploiting the dependence or association between index terms and documents.This concise representation of the whole dataset can be properly incorporated in the existing data representation. The use of prior knowledge about the nature oft he dataset helps in building a more efficient classifier for this set. This approach does not capture all the intricacies of text; however on some domains this technique substantially improves text classification accuracy.In this vein, a study of the interaction between supervised and unsupervised learning has been carried out. We have studied and implemented models that apply clustering in multiple ways and in conjunction with classification to construct robust text classifiers. The extensive experimentation has shown the effectiveness of using clustering to boost text classification performance. Additionally, preliminary experiments on some of the most important applications of text classification such as Spam Mail Filtering, Spam Detection in Social Bookmarking Systems,and Sentence Boundary Disambiguation, have shown promising enhancements by exploiting the proposed models.

scholarly journals A novel hybrid approach of SVM combined with NLP and probabilistic neural network for email phishing

2020 ◽  
Vol 10 (1) ◽  
pp. 486 ◽  
Author(s):  
Abhishek Kumar ◽  
Jyotir Moy Chatterjee ◽  
Vicente García Díaz
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Probabilistic Neural Network ◽  
Hybrid Approach ◽  
Cyber Attacks ◽  
Hybrid Analysis ◽  
Phishing Attacks ◽  
Phishing Detection ◽  
Hybrid Methodology

Phishing attacks are one of the slanting cyber-attacks that apply socially engineered messages that are imparted to individuals from expert hackers going for tricking clients to uncover their delicate data, the most mainstream correspondence channel to those messages is through clients' emails. Phishing has turned into a generous danger for web clients and a noteworthy reason for money related misfortunes. Therefore, different arrangements have been created to handle this issue. Deceitful emails, also called phishing emails, utilize a scope of impact strategies to convince people to react, for example, promising a fiscal reward or summoning a feeling of criticalness. Regardless of far reaching alerts and intends to instruct clients to distinguish phishing sends, these are as yet a pervasive practice and a worthwhile business. The creators accept that influence, as a style of human correspondence intended to impact others, has a focal job in fruitful advanced tricks. Cyber criminals have ceaselessly propelling their techniques for assault. The current strategies to recognize the presence of such malevolent projects and to keep them from executing are static, dynamic and hybrid analysis. In this work we are proposing a hybrid methodology for phishing detection incorporating feature extraction and classification of the mails using SVM. At last, alongside the chose features, the PNN characterizes the spam mails from the genuine mails with more exactness and accuracy.

scholarly journals GCIceNet: a graph convolutional network for accurate classification of water phases

2020 ◽  
Vol 22 (45) ◽  
pp. 26340-26350
Author(s):  
QHwan Kim ◽  
Joon-Hyuk Ko ◽  
Sunghoon Kim ◽  
Wonho Jhe
Keyword(s):  
Unsupervised Learning ◽  
Order Parameters ◽  
Water Molecules ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Supervised And Unsupervised Learning

We develop GCIceNet, which automatically generates machine-based order parameters for classifying the phases of water molecules via supervised and unsupervised learning with graph convolutional networks.

Sign in / Sign up

Export Citation Format

Share Document