A combination of clustering-based under-sampling with ensemble methods for solving imbalanced class problem in intelligent systems

2021 ◽  
Vol 169 ◽  
pp. 120796
Author(s):  
Mohammad Saleh Ebrahimi Shahabadi ◽  
Hamed Tabrizchi ◽  
Marjan Kuchaki Rafsanjani ◽  
B.B. Gupta ◽  
Francesco Palmieri
Keyword(s):  
Intelligent Systems ◽  
Ensemble Methods ◽  
Under Sampling ◽  
Imbalanced Class

scholarly journals COVID-19 County Level Severity Classification with Cl Imbalanced Class: A NearMiss Under-sampling Approach

2021 ◽  
Author(s):  
Timothy Oladunni ◽  
Sourou Tossou ◽  
Yayehyrad Haile ◽  
Adonias Kidane
Keyword(s):  
Class Imbalance ◽  
County Level ◽  
Policy Makers ◽  
Ensemble Models ◽  
Class A ◽  
Proposed Model ◽  
Under Sampling ◽  
Imbalanced Class ◽  
Severity Of The Disease ◽  
Sampling Approach

COVID-19 pandemic that broke out in the late 2019 has spread across the globe. The disease has infected millions of people. Thousands of lives have been lost. The momentum of the disease has been slowed by the introduction of vaccine. However, some countries are still recording high number of casualties. The focus of this work is to design, develop and evaluate a machine learning county level COVID-19 severity classifier. The proposed model will predict severity of the disease in a county into low, moderate, or high. Policy makers will find the work useful in the distribution of vaccines. Four learning algorithms (two ensembles and two non-ensembles) were trained and evaluated. Class imbalance was addressed using NearMiss under-sampling of the majority classes. The result of our experiment shows that the ensemble models outperformed the non-ensemble models by a considerable margin.

scholarly journals Development of glaucoma predictive model and risk factors assessment based on supervised models

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mahyar Sharifi ◽  
Toktam Khatibi ◽  
Mohammad Hassan Emamian ◽  
Somayeh Sadat ◽  
Hassan Hashemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Ensemble Methods ◽  
Learning Model ◽  
Training Dataset ◽  
Data Sampling ◽  
Machine Learning Model ◽  
Average Accuracy ◽  
Under Sampling ◽  
Bagging Ensemble

Abstract Objectives To develop and to propose a machine learning model for predicting glaucoma and identifying its risk factors. Method Data analysis pipeline is designed for this study based on Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. The main steps of the pipeline include data sampling, preprocessing, classification and evaluation and validation. Data sampling for providing the training dataset was performed with balanced sampling based on over-sampling and under-sampling methods. Data preprocessing steps were missing value imputation and normalization. For classification step, several machine learning models were designed for predicting glaucoma including Decision Trees (DTs), K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Random Forests (RFs), Extra Trees (ETs) and Bagging Ensemble methods. Moreover, in the classification step, a novel stacking ensemble model is designed and proposed using the superior classifiers. Results The data were from Shahroud Eye Cohort Study including demographic and ophthalmology data for 5190 participants aged 40-64 living in Shahroud, northeast Iran. The main variables considered in this dataset were 67 demographics, ophthalmologic, optometric, perimetry, and biometry features for 4561 people, including 4474 non-glaucoma participants and 87 glaucoma patients. Experimental results show that DTs and RFs trained based on under-sampling of the training dataset have superior performance for predicting glaucoma than the compared single classifiers and bagging ensemble methods with the average accuracy of 87.61 and 88.87, the sensitivity of 73.80 and 72.35, specificity of 87.88 and 89.10 and area under the curve (AUC) of 91.04 and 94.53, respectively. The proposed stacking ensemble has an average accuracy of 83.56, a sensitivity of 82.21, a specificity of 81.32, and an AUC of 88.54. Conclusions In this study, a machine learning model is proposed and developed to predict glaucoma disease among persons aged 40-64. Top predictors in this study considered features for discriminating and predicting non-glaucoma persons from glaucoma patients include the number of the visual field detect on perimetry, vertical cup to disk ratio, white to white diameter, systolic blood pressure, pupil barycenter on Y coordinate, age, and axial length.

scholarly journals The rationale for ensemble and meta-algorithmic architectures in signal and information processing

2015 ◽  
Vol 4 ◽  
Author(s):  
Steven J. Simske
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Data Storage ◽  
Intelligent Systems ◽  
Ensemble Methods ◽  
Learning Technologies ◽  
Machine Intelligence ◽  
Learning Approaches ◽  
Signal And Image Processing ◽  
Computing Power

We are living through an historic era in computing. As the price of data storage and processing continues to plummet, we are moving closer to a world where exhaustive search makes sense for certain types of intelligent systems. Signal and image processing are two related domains that benefit from this ubiquity of data storage and computing power. In this paper, a new, more collaborative, approach to solving signal and image processing tasks is built from the ground up to take into account the reality of this new age of data and computing superfluity. Starting with the mature field of ensemble methods and moving to the more-recently introduced field of meta-algorithmics, systems can be designed which are by nature to specifically incorporate new machine-learning technologies. These are more robust, more accurate, more adaptive, and ultimately less costly to build and maintain than the traditional machine-learning approaches. Applications to image and signal processing will then be discussed. Combined, these examples illustrate a new meta-architectural approach to the creation of machine intelligence systems.

Remote diagnostics of vibrations of dynamic objects by Doppler microwave sensors

Metrologiya ◽  
2020 ◽  
pp. 25-42
Author(s):  
Dmitrii V. Khablov
Keyword(s):  
Intelligent Systems ◽  
Electromagnetic Waves ◽  
Continuous Monitoring ◽  
Phase Changes ◽  
Vibration Diagnostics ◽  
Remote Diagnostics ◽  
Active Vibration ◽  
Dynamic Objects ◽  
Microwave Sensors ◽  
Non Destructive

This paper describes a promising method for non-contact vibration diagnostics based on the use of Doppler microwave sensors. In this case, active irradiation of the object with electromagnetic waves and the allocation of phase changes using two-channel quadrature processing of the received reflected signal are used. The modes of further fine analysis of the resulting signal using spectral or wavelet transformations depending on the nature of the active vibration are considered. The advantages of this non-contact and remote vibration analysis method for the study of complex dynamic objects are described. The convenience of the method for machine learning and use in intelligent systems of non-destructive continuous monitoring of the state of these objects by vibration is noted.

Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

2019 ◽  
pp. 21-30
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Interaction Analysis ◽  
Educational Data Mining ◽  
Main Task ◽  
Intellectual System ◽  
Will Force ◽  
To Come ◽  
Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

THE CONCEPT OF AUTONOMOUS SYSTEMS IN INDUSTRY 4.0

2019 ◽  
Vol 12 (1) ◽  
pp. 77-87
Author(s):  
György Kovács ◽  
Rabab Benotsmane ◽  
László Dudás
Keyword(s):  
Industry 4.0 ◽  
Intelligent Systems ◽  
Autonomous Systems ◽  
Essential Element ◽  
Industrial Sector ◽  
Life Cycles ◽  
Multi Agent Systems ◽  
New Paradigm ◽  
Intelligent Machines ◽  
Digital Network

Recent tendencies – such as the life-cycles of products are shorter while consumers require more complex and more unique final products – poses many challenges to the production. The industrial sector is going through a paradigm shift. The traditional centrally controlled production processes will be replaced by decentralized control, which is built on the self-regulating ability of intelligent machines, products and workpieces that communicate with each other continuously. This new paradigm known as Industry 4.0. This conception is the introduction of digital network-linked intelligent systems, in which machines and products will communicate to one another in order to establish smart factories in which self-regulating production will be established. In this article, at first the essence, main goals and basic elements of Industry 4.0 conception is described. After it the autonomous systems are introduced which are based on multi agent systems. These systems include the collaborating robots via artificial intelligence which is an essential element of Industry 4.0.

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