Binary Classification Using Linear SVM Pyramidal Tree

2010 ◽  
Author(s):  
M. Arun Kumar ◽  
M. Gopal
Keyword(s):  
Binary Classification ◽  
Linear Svm

scholarly journals A Neural Network Model for Attacker Detection using GRU and Modified Kernel of SVM

2019 ◽  
Vol 8 (2) ◽  
pp. 4441-4444
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Traditional Approach ◽  
Binary Classification ◽  
Support Vector ◽  
Output Layer ◽  
Proposed Model ◽  
Prediction Time ◽  
Linear Svm

over past few decades neural network changed the way of traditional computing many different models has proposed depending upon data intensity, predictions, and recognition and so on. Among which Gated Recurrent Unit (GRU) is created for variety of long short-term memory (LSTM) unit, which is part of recurrent neural network (RNN). These models proved to be dominant for range of machine learning job such as predictions, speech recognition, sentiment analysis and natural language processing. In this proposed model, a support vector machine (SVM) with modified kernel as final output layer for prediction is used instead of traditional approach of softmax and log loss function is used to calculate the loss. Proposed technique is applied for binary classification for intrusion detection using honeypot dataset (2013) network traffic sequence of Kyoto University. Results shows a prominent change in training efficiency of ≈89.45% and testing efficiency of ≈88.15% when compared with softmax output layer. We can conclude that linear SVM with modified kernel as output layer outperform compared with softmax in prediction time.

scholarly journals Transfer learning of ImageNet Object Classification Challenge features to image aesthetic binary classification

2019 ◽  
Author(s):  
Bruno Tinen ◽  
Jun Okamoto
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
State Of The Art ◽  
Binary Classification ◽  
Object Classification ◽  
Fine Tuning ◽  
Classification Problems ◽  
The Aesthetic ◽  
Linear Svm ◽  
High Level

The aesthetic classification of photographies is a problem of separating aesthetically pleasing images from not pleasing images using algorithms that describe and evaluate both emotional and technical factors. Since the mass adoption of deep convolutional neural network (DCNN) models for image classification problems different DCNN architectures have been developed due to its overall better performance, pushing the boundaries of the state-of-the-art performance of the image classification further. This paper evaluates how architectures and features that were primarily developed for the ImageNet Object Classification Challenge perform when analyzed under the aesthetic scope. A high level transfer learning model composed of a DCNN layer and a top layer that behaves as a linear SVM is proposed and seven different DCNN architectures are trained using it. Scenarios with just transfer learning and with fine tuning are evaluated and a model using the ResNet-Inception V2 architecture is proposed, which achieves results better than current state-of-the-art for the experiment conditions used.

Bases for Strategy Choice and Strategy Transitions in Binary Classification Tasks

2004 ◽  
Author(s):  
Lyle E. Bourne ◽  
Alice F. Healy ◽  
James A. Kole ◽  
William D. Raymond
Keyword(s):  
Binary Classification ◽  
Strategy Choice ◽  
Classification Tasks

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

METHODS OF MULTICLASSICATION OF BONE MARROW CELLS FOR THE DIAGNOSIS OF ACUTE LEUKEMIA AND MINIMAL RESIDUAL DISEASE (ON THE MATERIALS OF THE LABORATORY OF HEMATOPOIESIS OF N. N. BLOKHIN NATIONAL MEDICAL RESEARCH CENTER OF ONCOLOGY)

2020 ◽  
pp. 155-158
Author(s):  
Валентина Викторовна Дмитриева ◽  
Николай Николаевич Тупицын ◽  
Евгений Валерьевич Поляков ◽  
Софья Сергеевна Денисюк
Keyword(s):  
Bone Marrow ◽  
Acute Leukemia ◽  
Medical Research ◽  
Bone Marrow Cells ◽  
Residual Disease ◽  
Binary Classification ◽  
National Medical ◽  
Cell Class ◽  
Minimal Residual ◽  
Marrow Cells

Применение методов и средств цифровой обработки изображений при распознавании типов клеток крови и костного мозга для повышения качества диагностики острых лейкозов является актуальной научно-технической задачей, отвечающей стратегии развития технологий искусственного интеллекта в медицине. В работе предложен подход к мультиклассификации клеток костного мозга при диагностике острых лейкозов и минимальной остаточной болезни. Для проведения экспериментальных исследований сформирована выборка из 3284 изображений клеток, представленных Лабораторией гемопоэза Национального медицинского исследовательского центра онкологии им. Н.Н. Блохина. Предложенный подход к мультиклассификации клеток костного мозга основан на бинарной модели классификации для каждого из исследуемых классов относительно остальных. В рассматриваемой работе бинарная классификация выполняется методом опорных векторов. Метод мультиклассификации был программно реализован с применением интерпретатора Python 3.6.9. Входными данными программы служат файлы формата *.csv с таблицами морфологических, цветовых, текстурных признаков для каждой из клеток используемой выборки. В выборке представлено девять типов клеток костного мозга. Выходными данными программы мультиклассификации являются значения точности классификации на тестовой выборке, которые отражают совпадение прогнозируемого класса клетки с фактическим (верифицированным) классом клетки. “Эксперимент показал следующие результаты: точность мультиклассификации рассматриваемых типов клеток в среднем составила: 87% на тестовом наборе, 88% на обучающем наборе данных. Проведенное исследование является предварительным. В дальнейшем планируется увеличить число классов клеток, объем выборок различных типов клеток и с уточнением результатов мультиклассификации The use of methods and means of digital image processing in the recognition of types of blood cells and bone marrow to improve the quality of diagnosis of acute leukemia is an urgent scientific and technical task that meets the strategy for the development of artificial intelligence technologies in medicine. The paper proposes an approach to the multiclassification of bone marrow cells in the diagnosis of acute leukemia and minimal residual disease. For experimental studies, a sample of 3284 images of cells was formed, submitted by the Hematopoiesis Laboratory of the National Medical Research Center of Oncology named after V.I. N.N. Blokhin. The proposed approach to the multiclassification of bone marrow cells is based on a binary classification model for each of the studied classes relative to the others. In the work under consideration, binary classification is performed by the support vector machine. The multiclassification method was implemented programmatically using the Python 3.6.9 interpreter. The input data of the program are * .csv files with tables of morphological, color, texture features for each of the cells of the sample used. The sample contains nine types of bone marrow cells. The output data of the multiclassification program are the classification accuracy values on the test sample, which reflect the coincidence of the predicted cell class with the actual (verified) cell class. “The experiment showed the following results: the accuracy of multiclassification of the considered types of cells on average was: 87% on the test set, 88% on the training data set. This study is preliminary. In the future, it is planned to increase the number of classes of cells, the volume of samples of various types of cells and with the refinement of the results of multiclassification

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

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