scholarly journals Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 638 ◽  
Author(s):  
Simon Orozco-Arias ◽  
Johan S. Piña ◽  
Reinel Tabares-Soto ◽  
Luis F. Castillo-Ossa ◽  
Romain Guyot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transposable Elements ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Invariance Properties ◽  
Negative Class ◽  
Precision Recall Curve ◽  
And Training ◽  
Selection Of

Because of the promising results obtained by machine learning (ML) approaches in several fields, every day is more common, the utilization of ML to solve problems in bioinformatics. In genomics, a current issue is to detect and classify transposable elements (TEs) because of the tedious tasks involved in bioinformatics methods. Thus, ML was recently evaluated for TE datasets, demonstrating better results than bioinformatics applications. A crucial step for ML approaches is the selection of metrics that measure the realistic performance of algorithms. Each metric has specific characteristics and measures properties that may be different from the predicted results. Although the most commonly used way to compare measures is by using empirical analysis, a non-result-based methodology has been proposed, called measure invariance properties. These properties are calculated on the basis of whether a given measure changes its value under certain modifications in the confusion matrix, giving comparative parameters independent of the datasets. Measure invariance properties make metrics more or less informative, particularly on unbalanced, monomodal, or multimodal negative class datasets and for real or simulated datasets. Although several studies applied ML to detect and classify TEs, there are no works evaluating performance metrics in TE tasks. Here, we analyzed 26 different metrics utilized in binary, multiclass, and hierarchical classifications, through bibliographic sources, and their invariance properties. Then, we corroborated our findings utilizing freely available TE datasets and commonly used ML algorithms. Based on our analysis, the most suitable metrics for TE tasks must be stable, even using highly unbalanced datasets, multimodal negative class, and training datasets with errors or outliers. Based on these parameters, we conclude that the F1-score and the area under the precision-recall curve are the most informative metrics since they are calculated based on other metrics, providing insight into the development of an ML application.

scholarly journals Classification model for accuracy and intrusion detection using machine learning approach

2021 ◽  
Vol 7 ◽  
pp. e437
Author(s):  
Arushi Agarwal ◽  
Purushottam Sharma ◽  
Mohammed Alshehri ◽  
Ahmed A. Mohamed ◽  
Osama Alfarraj
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Detection System ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
K Nearest Neighbor

In today’s cyber world, the demand for the internet is increasing day by day, increasing the concern of network security. The aim of an Intrusion Detection System (IDS) is to provide approaches against many fast-growing network attacks (e.g., DDoS attack, Ransomware attack, Botnet attack, etc.), as it blocks the harmful activities occurring in the network system. In this work, three different classification machine learning algorithms—Naïve Bayes (NB), Support Vector Machine (SVM), and K-nearest neighbor (KNN)—were used to detect the accuracy and reducing the processing time of an algorithm on the UNSW-NB15 dataset and to find the best-suited algorithm which can efficiently learn the pattern of the suspicious network activities. The data gathered from the feature set comparison was then applied as input to IDS as data feeds to train the system for future intrusion behavior prediction and analysis using the best-fit algorithm chosen from the above three algorithms based on the performance metrics found. Also, the classification reports (Precision, Recall, and F1-score) and confusion matrix were generated and compared to finalize the support-validation status found throughout the testing phase of the model used in this approach.

scholarly journals Performance Evaluation of Different Machine Learning Classification Algorithms for Disease Diagnosis

2021 ◽  
Vol 12 (6) ◽  
pp. 1-28
Author(s):  
Munder Abdulatef Al-Hashem ◽  
Ali Mohammad Alqudah ◽  
Qasem Qananwah
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Machine Learning Classification

Knowledge extraction within a healthcare field is a very challenging task since we are having many problems such as noise and imbalanced datasets. They are obtained from clinical studies where uncertainty and variability are popular. Lately, a wide number of machine learning algorithms are considered and evaluated to check their validity of being used in the medical field. Usually, the classification algorithms are compared against medical experts who are specialized in certain disease diagnoses and provide an effective methodological evaluation of classifiers by applying performance metrics. The performance metrics contain four criteria: accuracy, sensitivity, and specificity forming the confusion matrix of each used algorithm. We have utilized eight different well-known machine learning algorithms to evaluate their performances in six different medical datasets. Based on the experimental results we conclude that the XGBoost and K-Nearest Neighbor classifiers were the best overall among the used datasets and signs can be used for diagnosing various diseases.

scholarly journals Performance Evaluation of Different Machine Learning Classification Algorithms for Diseases Diagnosis

2021 ◽  
Vol 12 (6) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Machine Learning Classification ◽  
Nearest Neighbor Classifiers

Knowledge extraction within a healthcare field is a very challenging task since we are having many problems such as noise and imbalanced datasets. They are obtained from clinical studies where uncertainty and variability are popular. Lately, a wide number of machine learning algorithms are considered and evaluated to check their validity of being used in the medical field. Usually, the classification algorithms are compared against medical experts who are specialized in certain disease diagnoses and provide an effective methodological evaluation of classifiers by applying performance metrics. The performance metrics contain four criteria: accuracy, sensitivity, and specificity forming the confusion matrix of each used algorithm. We have utilized eight different well-known machine learning algorithms to evaluate their performances in six different medical datasets. Based on the experimental results we conclude that the XGBoost and K-Nearest Neighbor classifiers were the best overall among the used datasets and signs can be used for diagnosing various diseases.

scholarly journals An Investigation of Machine Learning Algorithms on COVID-19 Dataset 

2020 ◽  
Author(s):  
Prasannavenkatesan Theerthagiri ◽  
I.Jeena Jacob ◽  
A.Usha Ruby ◽  
Y.Vamsidhar
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
True Positive Rate ◽  
Receiver Operator Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Kappa Score ◽  
Machine Learning Classification ◽  
Positive Rate

Abstract This paper studies the different machine learning classification algorithms to predict the COVID-19 recovered and deceased cases. The k-fold cross-validation resampling technique is used to validate the prediction model. The prediction scores of each algorithm are evaluated with performance metrics such as prediction accuracy, precision, recall, mean square error, confusion matrix, and kappa score. For the given dataset, the k-nearest neighbour (KNN) classification algorithm produces 80.4 % of predication accuracy and 1.5 to 3.3 % of improved accuracy over other algorithms. The KNN algorithm predicts 92 % (true positive rate) of the deceased cases correctly with 0.077 % of misclassification. Further, the KNN algorithm produces the lowest error rate as 0.19 on the prediction of accurate COVID-19 cases than the other algorithm. Also, it produces the receiver operator characteristic curve with the output value of 82 %.

scholarly journals Selection of Suitable Machine Learning Algorithms for Classification Tasks in Reverse Logistics

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 272-277
Author(s):  
Hannah Lickert ◽  
Aleksandra Wewer ◽  
Sören Dittmann ◽  
Pinar Bilge ◽  
Franz Dietrich
Keyword(s):  
Machine Learning ◽  
Reverse Logistics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Tasks ◽  
Selection Of

AI-Enabled Support System for Melanoma Detection and Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 58-75
Author(s):  
Vivek Sen Saxena ◽  
Prashant Johri ◽  
Avneesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Skin Lesion ◽  
Performance Metrics ◽  
Similarity Index ◽  
Skin Lesions ◽  
Machine Learning Algorithms ◽  
Lesion Area ◽  
Melanoma Detection ◽  
Grabcut Algorithm

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

scholarly journals Attack and Anomaly Detection in IoT Networks Using Supervised Machine Learning Approaches

2021 ◽  
Vol 35 (1) ◽  
pp. 11-21
Author(s):  
Himani Tyagi ◽  
Rajendra Kumar
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Detection System ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Testing Time ◽  
Learning Approaches ◽  
Reduction Techniques ◽  
Share Data

IoT is characterized by communication between things (devices) that constantly share data, analyze, and make decisions while connected to the internet. This interconnected architecture is attracting cyber criminals to expose the IoT system to failure. Therefore, it becomes imperative to develop a system that can accurately and automatically detect anomalies and attacks occurring in IoT networks. Therefore, in this paper, an Intrsuion Detection System (IDS) based on extracted novel feature set synthesizing BoT-IoT dataset is developed that can swiftly, accurately and automatically differentiate benign and malicious traffic. Instead of using available feature reduction techniques like PCA that can change the core meaning of variables, a unique feature set consisting of only seven lightweight features is developed that is also IoT specific and attack traffic independent. Also, the results shown in the study demonstrates the effectiveness of fabricated seven features in detecting four wide variety of attacks namely DDoS, DoS, Reconnaissance, and Information Theft. Furthermore, this study also proves the applicability and efficiency of supervised machine learning algorithms (KNN, LR, SVM, MLP, DT, RF) in IoT security. The performance of the proposed system is validated using performance Metrics like accuracy, precision, recall, F-Score and ROC. Though the accuracy of Decision Tree (99.9%) and Randon Forest (99.9%) Classifiers are same but other metrics like training and testing time shows Random Forest comparatively better.

scholarly journals Supermarket Sales Prediction Using Regression

2021 ◽  
Vol 10 (2) ◽  
pp. 1153-1157
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Customer Data ◽  
Sales Data ◽  
Online Marketplace ◽  
Sales Prediction ◽  
The Future

Sales forecasting is an important when it comes to companies who are engaged in retailing, logistics, manufacturing, marketing and wholesaling. It allows companies to allocate resources efficiently, to estimate revenue of the sales and to plan strategies which are better for company’s future. In this paper, predicting product sales from a particular store is done in a way that produces better performance compared to any machine learning algorithms. The dataset used for this project is Big Mart Sales data of the 2013.Nowadays shopping malls and Supermarkets keep track of the sales data of the each and every individual item for predicting the future demand of the customer. It contains large amount of customer data and the item attributes. Further, the frequent patterns are detected by mining the data from the data warehouse. Then the data can be used for predicting the sales of the future with the help of several machine learning techniques (algorithms) for the companies like Big Mart. In this project, we propose a model using the Xgboost algorithm for predicting sales of companies like Big Mart and founded that it produces better performance compared to other existing models. An analysis of this model with other models in terms of their performance metrics is made in this project. Big Mart is an online marketplace where people can buy or sell or advertise your merchandise at low cost. The goal of the paper is to make Big Mart the shopping paradise for the buyers and a marketing solutions for the sellers as well. The ultimate aim is the complete satisfaction of the customers. The project “SUPERMARKET SALES PREDICTION” builds a predictive model and finds out the sales of each of the product at a particular store. The Big Mart use this model to under the properties of the products which plays a major role in increasing the sales. This can also be done on the basis hypothesis that should be done before looking at the data

Machine-Learning-Based External Plagiarism Detecting Methodology From Monolingual Documents

2018 ◽  
pp. 122-139
Author(s):  
Saugata Bose ◽  
Ritambhra Korpal
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Confusion Matrix ◽  
False Negative ◽  
False Negative Rate ◽  
Search Space ◽  
Machine Learning Algorithms ◽  
C4.5 Decision Tree ◽  
N Gram ◽  
Four Levels

In this chapter, an initiative is proposed where natural language processing (NLP) techniques and supervised machine learning algorithms have been combined to detect external plagiarism. The major emphasis is on to construct a framework to detect plagiarism from monolingual texts by implementing n-gram frequency comparison approach. The framework is based on 120 characteristics which have been extracted during pre-processing steps using simple NLP approach. Afterward, filter metrics has been applied to select most relevant features and supervised classification learning algorithm has been used later to classify the documents in four levels of plagiarism. Then, confusion matrix was built to estimate the false positives and false negatives. Finally, the authors have shown C4.5 decision tree-based classifier's suitability on calculating accuracy over naive Bayes. The framework achieved 89% accuracy with low false positive and false negative rate and it shows higher precision and recall value comparing to passage similarities method, sentence similarity method, and search space reduction method.

scholarly journals Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

2019 ◽  
Vol 9 (18) ◽  
pp. 3665 ◽  
Author(s):  
Ahmet Çağdaş Seçkin ◽  
Aysun Coşkun
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Deductive Reasoning ◽  
Real Life ◽  
Indoor Positioning ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Positioning Systems ◽  
Common Solution ◽  
Variable Reduction

Wi-Fi-based indoor positioning offers significant opportunities for numerous applications. Examining the Wi-Fi positioning systems, it was observed that hundreds of variables were used even when variable reduction was applied. This reveals a structure that is difficult to repeat and is far from producing a common solution for real-life applications. It aims to create a common and standardized dataset for indoor positioning and localization and present a system that can perform estimations using this dataset. To that end, machine learning (ML) methods are compared and the results of successful methods with hierarchical inclusion are then investigated. Further, new features are generated according to the measurement point obtained from the dataset. Subsequently, learning models are selected according to the performance metrics for the estimation of location and position. These learning models are then fused hierarchically using deductive reasoning. Using the proposed method, estimation of location and position has proved to be more successful by using fewer variables than the current studies. This paper, thus, identifies a lack of applicability present in the research community and solves it using the proposed method. It suggests that the proposed method results in a significant improvement for the estimation of floor and longitude.

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