Enhancing the Classification of Eye Bacteria Using Bagging to Multilayer Perceptron and Decision Tree

2011 ◽  
pp. 277-293
Author(s):  
Xu-Qin Li ◽  
Evor L. Hines ◽  
Mark S. Leeson ◽  
D. D. Iliescu
Keyword(s):  
Decision Tree ◽  
Electronic Nose ◽  
Multilayer Perceptron ◽  
Misclassification Rate ◽  
Eye Disease ◽  
Multi Layer Perceptron

Eye bacteria are vital to the diagnosis of eye disease, which makes the classification of such bacteria necessary and important. This chapter aims to classify different kinds of eye bacteria after the data were collected by an Electronic Nose. First the Multi-layer perceptron (MLP) and decision tree (DT) were introduced as the algorithm and the base classifiers. After that, the bagging technique was introduced to both algorithms and showed that the accuracy of the MLP had been significantly improved. Moreover, bagging to the DT not only reduced the misclassification rate, but enabled DT to select the most important features, and thus, decreased the dimension of the data facilitating an enhanced training and testing process.

DIRECT EXPLANATIONS FOR THE DEVELOPMENT AND USE OF A MULTI-LAYER PERCEPTRON NETWORK THAT CLASSIFIES LOW-BACK-PAIN PATIENTS

2001 ◽  
Vol 11 (04) ◽  
pp. 335-347 ◽  
Author(s):  
M. L. VAUGHN ◽  
S. J. CAVILL ◽  
S. J. TAYLOR ◽  
M. A. FOY ◽  
A. J. B. FOGG
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Real World ◽  
Multilayer Perceptron ◽  
Test Cases ◽  
Low Back ◽  
Multi Layer Perceptron ◽  
Mlp Network ◽  
Pain Patients

Using a new method published by the first author, this article shows how direct explanations can be provided to interpret the classification of any input case by a standard multilayer perceptron (MLP) network. The method is demonstrated for a real-world MLP that classifies low-back-pain patients into three diagnostic classes. The application of the method leads to the discovery of a number of mis-diagnosed training and test cases and to the development of a more optimal low-back-pain MLP network.

SETTING UP A PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF HIGHWAY VEHICLES

2005 ◽  
Vol 05 (04) ◽  
pp. 411-423 ◽  
Author(s):  
MAJURA F. SELEKWA ◽  
VALERIAN KWIGIZILE ◽  
RENATUS N. MUSSA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Tree ◽  
Probabilistic Neural Network ◽  
Classification Problem ◽  
Misclassification Rate ◽  
Tree Methods ◽  
Decision Tree Methods

Many neural network methods used for efficient classification of populations work only when the population is globally separable. In situ classification of highway vehicles is one of the problems with globally nonseparable populations. This paper presents a systematic procedure for setting up a probabilistic neural network that can classify the globally nonseparable population of highway vehicles. The method is based on a simple concept that any set of classifiable data can be broken down to subclasses of locally separable data. Hence, if these locally separable data can be identified, then the classification problem can be carried out in two hierarchical steps; step one classifies the data according to the local subclasses, and step two classifies the local subclasses into the global classes. The proposed approach was tested on the problem of classifying highway vehicles according to the US Federal Highway Administration standard, which is normally handled by decision tree methods that use vehicle axle information and a set of IF-THEN rules. By using a sample of 3326 vehicles, the proposed method showed improved classification results with an overall misclassification rate of only 2.9% compared to 9.7% of the decision tree methods. A similar setup can be used with different neural networks such as recurrent neural networks, but they were not tested in this study especially since the focus was for in situ applications where a high learning rate is desired.

scholarly journals The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 263
Author(s):  
Samreen Naeem ◽  
Aqib Ali ◽  
Christophe Chesneau ◽  
Muhammad H. Tahir ◽  
Farrukh Jamal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medicinal Plant ◽  
Texture Feature ◽  
Stevia Rebaudiana ◽  
Ocimum Sanctum ◽  
Multi Layer Perceptron ◽  
Plant Leaves ◽  
Chi Square ◽  
Lemon Balm

This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia.

scholarly journals Decision Tree C 4.5 Algorithm for Classification of Poor Family Scholarship Recipients

2021 ◽  
Vol 1125 (1) ◽  
pp. 012048
Author(s):  
Y Kustiyahningsih ◽  
B K Khotimah ◽  
D R Anamisa ◽  
M Yusuf ◽  
T Rahayu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Poor Family ◽  
Scholarship Recipients

scholarly journals 174 A comparison of machine learning algorithms in the classification of beef steers finished in feedlot

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 126-127
Author(s):  
Lucas S Lopes ◽  
Christine F Baes ◽  
Dan Tulpan ◽  
Luis Artur Loyola Chardulo ◽  
Otavio Machado Neto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Final Decision ◽  
Relevant Parameter ◽  
Good Prediction ◽  
Quality Traits ◽  
C4.5 Decision Tree

Abstract The aim of this project is to compare some of the state-of-the-art machine learning algorithms on the classification of steers finished in feedlots based on performance, carcass and meat quality traits. The precise classification of animals allows for fast, real-time decision making in animal food industry, such as culling or retention of herd animals. Beef production presents high variability in its numerous carcass and beef quality traits. Machine learning algorithms and software provide an opportunity to evaluate the interactions between traits to better classify animals. Four different treatment levels of wet distiller’s grain were applied to 97 Angus-Nellore animals and used as features for the classification problem. The C4.5 decision tree, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP) Artificial Neural Network algorithms were used to predict and classify the animals based on recorded traits measurements, which include initial and final weights, sheer force and meat color. The top performing classifier was the C4.5 decision tree algorithm with a classification accuracy of 96.90%, while the RF, the MLP and NB classifiers had accuracies of 55.67%, 39.17% and 29.89% respectively. We observed that the final decision tree model constructed with C4.5 selected only the dry matter intake (DMI) feature as a differentiator. When DMI was removed, no other feature or combination of features was sufficiently strong to provide good prediction accuracies for any of the classifiers. We plan to investigate in a follow-up study on a significantly larger sample size, the reasons behind DMI being a more relevant parameter than the other measurements.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

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