scholarly journals Supervised Classification of Diseases Based on an Improved Associative Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1458
Author(s):  
Raúl Jiménez-Cruz ◽  
José-Luis Velázquez-Rodríguez ◽  
Itzamá López-Yáñez ◽  
Yenny Villuendas-Rey ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Performance Measure ◽  
Support Vector ◽  
Mathematical Tool ◽  
Classifier Ensembles ◽  
Orthogonal Projections ◽  
Vector Machines ◽  
Supervised Classifiers ◽  
Classification Of Diseases ◽  
Low Performance ◽  
Value Decomposition

The linear associator is a classic associative memory model. However, due to its low performance, it is pertinent to note that very few linear associator applications have been published. The reason for this is that this model requires the vectors representing the patterns to be orthonormal, which is a big restriction. Some researchers have tried to create orthogonal projections to the vectors to feed the linear associator. However, this solution has serious drawbacks. This paper presents a proposal that effectively improves the performance of the linear associator when acting as a pattern classifier. For this, the proposal involves transforming the dataset using a powerful mathematical tool: the singular value decomposition. To perform the experiments, we selected fourteen medical datasets of two classes. All datasets exhibit balance, so it is possible to use accuracy as a performance measure. The effectiveness of our proposal was compared against nine supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, support vector machines, and neural networks), including three classifier ensembles. The Friedman and Holm tests show that our proposal had a significantly better performance than four of the nine classifiers. Furthermore, there are no significant differences against the other five, although three of them are ensembles.

scholarly journals A Novel and Simple Mathematical Transform Improves the Perfomance of Lernmatrix in Pattern Classification

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 732
Author(s):  
José-Luis Velázquez-Rodríguez ◽  
Yenny Villuendas-Rey ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Pattern Classification ◽  
State Of The Art ◽  
Class Imbalance ◽  
Logistic Function ◽  
Performance Measure ◽  
Support Vector ◽  
Friedman Test ◽  
Vector Machines ◽  
Supervised Classifiers ◽  
Post Hoc

The Lernmatrix is a classic associative memory model. The Lernmatrix is capable of executing the pattern classification task, but its performance is not competitive when compared to state-of-the-art classifiers. The main contribution of this paper consists of the proposal of a simple mathematical transform, whose application eliminates the subtractive alterations between patterns. As a consequence, the Lernmatrix performance is significantly improved. To perform the experiments, we selected 20 datasets that are challenging for any classifier, as they exhibit class imbalance. The effectiveness of our proposal was compared against seven supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, logistic function, support vector machines, and neural networks). By choosing balanced accuracy as a performance measure, our proposal obtained the best results in 10 datasets. The elimination of subtractive alterations makes the new model competitive against the best classifiers, and sometimes beats them. After applying the Friedman test and the Holm post hoc test, we can conclude that within a 95% confidence, our proposal competes successfully with the most effective classifiers of the state of the art.

Combination with Machine Learning Algorithms for the Classification in E-Bussiness

2011 ◽  
Vol 230-232 ◽  
pp. 625-628
Author(s):  
Lei Shi ◽  
Xin Ming Ma ◽  
Xiao Hong Hu
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Mathematical Tool ◽  
Vector Machines

E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases, browsing pattern and preferences has been generated. Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness. Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In this paper, rough set theory and support vector machines are combined to construct a classification model to classify the data of E-bussiness effectively.

scholarly journals Dynamic Nearest Neighbor: An Improved Machine Learning Classifier and Its Application in Finances

2021 ◽  
Vol 11 (19) ◽  
pp. 8884
Author(s):  
Oscar Camacho-Urriolagoitia ◽  
Itzamá López-Yáñez ◽  
Yenny Villuendas-Rey ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Support Vector ◽  
Human Beings ◽  
Classifier Ensembles ◽  
Learning Classifier ◽  
Learning Techniques ◽  
Supervised Classifiers ◽  
Real World Datasets

The presence of machine learning, data mining and related disciplines is increasingly evident in everyday environments. The support for the applications of learning techniques in topics related to economic risk assessment, among other financial topics of interest, is relevant for us as human beings. The content of this paper consists of a proposal of a new supervised learning algorithm and its application in real world datasets related to finance, called D1-NN (Dynamic 1-Nearest Neighbor). The D1-NN performance is competitive against the main state of the art algorithms in solving finance-related problems. The effectiveness of the new D1-NN classifier was compared against five supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, classifier ensembles, and neural networks), with superior results overall.

scholarly journals Students’ Class Performance Prediction Using Machine Learning Classifiers

2021 ◽  
Vol 19 (1) ◽  
pp. 112-121
Author(s):  
Adeel Ahmed ◽  
Kamlesh Kumar ◽  
Mansoor A. Khuhro ◽  
Asif A. Wagan ◽  
Imtiaz A. Halepoto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Academic Performance ◽  
Educational Data Mining ◽  
Performance Measure ◽  
Faculty Members ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Vector Machines ◽  
Academic Records

Nowadays, educational data mining is being employed as assessing tool for study and analysis of hidden patterns in academic databases which can be used to predict student’s academic performance. This paper implements various machine learning classification techniques on students’ academic records for results predication. For this purpose, data of MS(CS) students were collected from a public university of Pakistan through their assignments, quizzes, and sessional marks. The WEKA data mining tool has been used for performing all experiments namely, data pre-processing, classification, and visualization. For performance measure, classifier models were trained with 3- and 10-fold cross validation methods to evaluate classifiers' accuracy. The results show that bagging classifier combined with support vector machines outperform other classifiers in terms of accuracy, precision, recall, and F-measure score. The obtained outcomes confirm that our research provides significant contribution in prediction of students’ academic performance which can ultimately be used to assists faculty members to focus low grades students in improving their academic records.

Digital Watermarking Techniques for Images

2014 ◽  
pp. 737-746
Author(s):  
Channapragada R. S. G. Rao ◽  
Vadlamani Ravi ◽  
Munaga. V. N. K. Prasad ◽  
E. V. Gopal
Keyword(s):  
Support Vector Machines ◽  
Singular Value Decomposition ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Singular Value ◽  
Independent Component ◽  
Support Vector ◽  
Vector Machines ◽  
Value Decomposition ◽  
Cryptographic Techniques

This Chapter presents a brief review of the work done during 1990-2013, in the application of intelligent techniques and independent component analysis to digital image watermarking. The review considers only the gray-scale and color images excluding other multimedia. The intelligent techniques considered are support vector machines, singular value decomposition and cryptographic techniques. The review is structured by considering the type of technique applied to solve the problem as an important dimension. Consequently the papers are grouped into the following four families, (i) Support vector machines, (ii) Singular value decomposition and (iii) Cryptographic Techniques (iv) Independent component analysis. Comparative analysis of different techniques is also presented. Finally, the review is concluded with future directions.

scholarly journals Satellite image classification and quality parame-ters using ML classifier

2018 ◽  
Vol 7 (1.8) ◽  
pp. 6
Author(s):  
K. Radhika ◽  
S. Varadarajan
Keyword(s):  
Maximum Likelihood ◽  
Satellite Images ◽  
Performance Metrics ◽  
Satellite Image ◽  
Feature Space ◽  
Support Vector ◽  
Vector Machines ◽  
Supervised Classifiers ◽  
Multispectral Satellite Images

Remote sensing images are an important source of information regarding the Earth surface. For many applications like geology, urban planning, forest and land cover/land use, the underlying information from such images is needed. Extraction of this information is usually achieved through a classification process which is one of the most powerful tools in digital image processing. Good classifier is required to extract the information in satellite images. Latest methods used for classification of pixels in multispectral satellite images are supervised classifiers such as Support Vector Machines (SVM), k-Nearest Number (K-NN) and Maximum Likelihood (ML) classifier. SVM may be one-class SVM or multi-class SVM. K-NN is simple technique in high-dimensional feature space. In ML classifier, classification is based on the maximum likelihood of the pixel. The performance metrics for these classifiers are calculated and compared. Totally 200 points have been considered for validation purpose.

scholarly journals Evolutionary Optimization of Ensemble Learning to Determine Sentiment Polarity in an Unbalanced Multiclass Corpus

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1020
Author(s):  
Consuelo V. García-Mendoza ◽  
Omar J. Gambino ◽  
Miguel G. Villarreal-Cervantes ◽  
Hiram Calvo
Keyword(s):  
Language Processing ◽  
Differential Evolution Algorithm ◽  
Weighting Scheme ◽  
Support Vector ◽  
Classifier Ensembles ◽  
Test Set ◽  
Spanish Society ◽  
Learning Techniques ◽  
Vector Machines ◽  
Polarity Classification

Sentiment polarity classification in social media is a very important task, as it enables gathering trends on particular subjects given a set of opinions. Currently, a great advance has been made by using deep learning techniques, such as word embeddings, recurrent neural networks, and encoders, such as BERT. Unfortunately, these techniques require large amounts of data, which, in some cases, is not available. In order to model this situation, challenges, such as the Spanish TASS organized by the Spanish Society for Natural Language Processing (SEPLN), have been proposed, which pose particular difficulties: First, an unwieldy balance in the training and the test set, being this latter more than eight times the size of the training set. Another difficulty is the marked unbalance in the distribution of classes, which is also different between both sets. Finally, there are four different labels, which create the need to adapt current classifications methods for multiclass handling. Traditional machine learning methods, such as Naïve Bayes, Logistic Regression, and Support Vector Machines, achieve modest performance in these conditions, but used as an ensemble it is possible to attain competitive execution. Several strategies to build classifier ensembles have been proposed; this paper proposes estimating an optimal weighting scheme using a Differential Evolution algorithm focused on dealing with particular issues that multiclass classification and unbalanced corpora pose. The ensemble with the proposed optimized weighting scheme is able to improve the classification results on the full test set of the TASS challenge (General corpus), achieving state of the art performance when compared with other works on this task, which make no use of NLP techniques.

scholarly journals Denoising and SAR Image Classification with K-SVD Algorithm

2018 ◽  
Vol 7 (3.3) ◽  
pp. 36 ◽  
Author(s):  
D V.R Mohan ◽  
I Rambabu ◽  
B Harish
Keyword(s):  
Support Vector ◽  
Gray Level ◽  
Infrared Sensors ◽  
Sar Images ◽  
Feature Spaces ◽  
Vector Machines ◽  
And Performance ◽  
Occurrence Matrix ◽  
Value Decomposition

Synthetic Aperture Radar (SAR) is not only having the characteristic of obtaining images during all-day, all-weather, but also provides object information which is distinctive from visible and infrared sensors. but, SAR images have more speckles noise and fewer bands. This paper propose a method for denoising, feature extraction and classification of SAR images. Initially the image was denoised using K-Singular Value Decomposition (K-SVD) algorithm. Then the Gray Level Histogram (GLH) and Gray Level Co-occurrence Matrix (GLCM) are used for extraction of features. Secondly, the extracted feature vectors from the first step were combined using the correlation analysis to decrease the dimensionality of the feature spaces. Thirdly, Classification of SAR images was done in Sparse Representations Classification (SRC) and Support Vector Machines (SVMs). The results indicate that the performance of the introduce SAR classification method is good. The above mentioned classifications techniques are enhanced and performance parameters are computed using MATLAB 2014a software.  

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