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Incorporating Unsupervised Machine Learning Technique on Genetic Algorithm for Test Case Optimization
2018
The International Arab Journal of Information Technology
unpublished
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This paper discusses on test case selection and prioritization by combining genetic and clustering algorithms. ...
Test cases have been generated using genetic algorithm and the prioritization is performed using group-wise clustering algorithm by assigning priorities to the generated test cases thereby reducing the ...
Incorporating Unsupervised Machine Learning Technique on Genetic Algorithm for Test Case Optimization 1. ...
fatcat:5giujeiosjay7avlp33lhvm7hq
An Approach For Unsupervised Feature Selection Using Genetic Algorithm
2016
Zenodo
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Hereafter plummeting the dimensionality of dataset is principal and imperative job for data mining applications and machine learning algorithms in order that computational burden of the learning algorithms ...
In our proposed method we have incorporated the Genetic feature selection method and GFS and TPR (True Positive Rate), FNR (False Negative Rate) estimated using KNN Classifier. q ...
In this practice the target is achieved typically by clustering technique. By unsupervised learning we stand for unsupervised clustering. ...
doi:10.5281/zenodo.55985
fatcat:ouxwfm7lj5f2ph2udbo5day2ru
A Genetic Algorithm Approach for Semi-Supervised Clustering
2002
International Journal of Smart Engineering System Design
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In this work, a genetic algorithm is proposed to optimize such an objective function to produce clusters. Non-empty clusters are labeled with the majority class. ...
A novel semi-supervised clustering algorithm is proposed that synergizes the benefits of supervised and unsupervised learning methods. ...
Many thanks to the referees for their helpful comments. ...
doi:10.1080/10255810210623
fatcat:y5pvlvkje5akpa3ohklojuhuhm
Identifying Patterns of Breast Cancer Genetic Signatures using Unsupervised Machine Learning
2019
2019 IEEE International Conference on Imaging Systems and Techniques (IST)
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The classification of cancer types is based on unsupervised feature learning. ...
Unsupervised clustering use matrix algebra based on similarity measures which made it suitable for analyzing gene expression. ...
The authors would like to acknowledge OpenUAE Research and Development group at the University of Sharjah and Al-Jalila Foundation (Grant code: AJF201741) for funding this work. ...
doi:10.1109/ist48021.2019.9010510
dblp:conf/ist/HamoudiBAHNN19
fatcat:rng3wg3rafe6xdmylvyzpmoydm
Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions
2020
Frontiers in Genetics
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The study categorizes various feature selection algorithms under Supervised, Unsupervised, and Semi-supervised learning. ...
This paper does an extensive review of the various works done on Machine Learning-based gene selection in recent years, along with its performance analysis. ...
The Gene Selection based on machine learning can be classified into three types, Supervised, Unsupervised, and Semi-Supervised. ...
doi:10.3389/fgene.2020.603808
pmid:33362861
pmcid:PMC7758324
fatcat:jhyfsc72tngwhnrl4vxg3k4tii
On the Applicability of Soft Computing Techniques in Regression Testing
2017
International Journal of Computer Applications
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The work also presents computational intelligence based test case prioritization techniques. The work reviews test case prioritization based on Neural Networks, Genetic Algorithms and Fuzzy Logic. ...
Test case prioritization technique re-orders the test cases in such a way that important test cases are executed within the resources. The work reviews different test case prioritization techniques. ...
[16] , proposed a framework for test case prioritization using machine learning and program slicing. ...
doi:10.5120/ijca2017916042
fatcat:4jqpgqltt5aw7hlfsgw3pfivnq
Credit Card Fraud Detection using Deep Learning based on Neural Network and Auto-encoder
2020
International Journal of Engineering and Advanced Technology
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An unsupervised algorithm is used to detect online transactions, as fraudsters commit fraud once by online media and then move on to other techniques. ...
Thus, Artificial Intelligent (AI) algorithms are used to detect the behavior of such activity by learning the past behavior of the transaction of the users. ...
In the future, one can further fine-tuning hyperparameters the neural network, perform boosting techniques on different Machine Learning algorithms. ...
doi:10.35940/ijeat.e9934.069520
fatcat:67pmckkxhjecdhe56adttiutpe
Comparative Study of Microarray Based Disease Prediction - A Survey
2019
International Journal of Scientific Research in Computer Science Engineering and Information Technology
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Recognition of genetic expression becomes an important issue for research while diagnosing genetic diseases. ...
To solve this problem feature selection process which optimally extracts the features is introduced in clustering in classification techniques. ...
Unsupervised learning algorithm: Unsupervised learning is a branch of machine learning that learns from test data that has not been labeled, classified or categorized. ...
doi:10.32628/cseit195435
fatcat:jpbopg3mmvczpm4q3bqcdk55pq
Predictive and Prescriptive Analytics in Healthcare: A Survey
2020
Procedia Computer Science
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Thus, this article aims to identify the advances that have been made in this area, focusing on the development of predictive and optimization techniques, applied in Health, and how these can improve the ...
Thus, this article aims to identify the advances that have been made in this area, focusing on the development of predictive and optimization techniques, applied in Health, and how these can improve the ...
Figure 1 - 1 Three Types of Machine Learning Algorithms. Withdrawn from
Figure 2 - 2 Top Supervised Learning Machine Learning Methods. ...
doi:10.1016/j.procs.2020.03.078
fatcat:futr73koafexrfidmh3x53enp4
Evolving Ensembles of Feature Subsets towards Optimal Feature Selection for Unsupervised and Semi-supervised Clustering
[chapter]
2010
Lecture Notes in Computer Science
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The work in unsupervised learning centered on clustering has been extended with new paradigms to address the demands raised by real-world problems. ...
The method makes use of an ensemble of near-optimal feature subsets delivered by a multi-modal genetic algorithm in order to quantify the relative importance of each feature to clustering. ...
the reported comparisons with their extensive studies in unsupervised feature selection and clustering. ...
doi:10.1007/978-3-642-13025-0_8
fatcat:u46pj4fsxngihctwg7eixdv6wa
GENETIC MARKERS AND BIOSTATISTICAL METHODS AS APPROPRIATE TOOLS TO PRESERVE GENETIC RESOURCES
2018
AGROFOR
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classes on the basis of its pattern of measurements.Large scale of supervised learning oriented method was used for traceability andidentification on individual level. ...
The use of DNA markers has been shown to be auseful tool for individual identification. It is necessary to use modern statisticalmethod based on data mining and supervised learning. ...
The approach used for populations' structure assessment is characterized as unsupervised learning methods with specific computation algorithm. ...
doi:10.7251/agreng1802041k
fatcat:eu2tzimsynh7rcty4an64feoj4
A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems
2020
IEEE Access
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Our novel approach sheds light on its features and allows users to study and analyze the user requirements and determine both the function of objects related to the system and the machine learning algorithms ...
The SEMLHI framework includes four modules (software, machine learning, machine learning algorithms, and health informatics data) that organize the tasks in the framework using a SEMLHI methodology, thereby ...
Awwad, for allowing us to access the Palestinian dataset for patients, and for all the teams that supported us during the last two years, the feedback from whom greatly improved this manuscript. ...
doi:10.1109/access.2020.2970178
fatcat:6q5rf5hcbbdpzb6zr6wn3ouvyu
A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem
2020
Mathematics
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In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning ...
The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. ...
On the other hand, machine-learning techniques correspond to algorithms capable of learning from a dataset [36] . ...
doi:10.3390/math8040555
fatcat:px4dq27cbbgddcsnz4b7qdkone
Machine Learning Techniques for Anomaly Detection: An Overview
2013
International Journal of Computer Applications
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This paper presents an overview of research directions for applying supervised and unsupervised methods for managing the problem of anomaly detection. ...
Intrusion detection has gain a broad attention and become a fertile field for several researches, and still being the subject of widespread interest by researchers. ...
Since UNC is based on genetic optimization, it is much less susceptible to suboptimal solutions than traditional techniques. ...
doi:10.5120/13715-1478
fatcat:rjkjycwknbclzgvfjp24cg5ohi
Credit Card Fraud Detection using Machine Learning
2019
International Journal of Engineering and Advanced Technology
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This paper investigates naïve bayesian, k-nearest neighbor's performance on highly skewed credit card fraud based on genetic and optimization algorithm to determine the fraudulent transaction using credit ...
Present day strategies dependent on Artificial Intelligence, Data mining, Fuzzy rationale, Machine learning, Sequence Alignment, Genetic Programming and so forth., are developed in distinguishing different ...
Machine Learning Using Optimization Algorithm Optimization Algorithms are organized through their interactions with the analysis of machine learning and knowledge. ...
doi:10.35940/ijeat.b4957.129219
fatcat:4khkmer67bczfcir2tnhg2qihe
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