scholarly journals ELECTRE tree: a machine learning approach to infer ELECTRE Tri-B parameters

2021 ◽  
Vol 55 (4) ◽  
pp. 586-608
Author(s):  
Gabriela Montenegro Montenegro de Barros ◽  
Valdecy Pereira ◽  
Marcos Costa Roboredo
Keyword(s):  
Machine Learning ◽  
Design Methodology ◽  
Majority Vote ◽  
Content Type ◽  
Ensemble Technique ◽  
Machine Learning Approach ◽  
Electre Tri ◽  
Criteria Weights ◽  
Second Procedure ◽  
Decision Boundaries

PurposeThis paper presents an algorithm that can elicitate (infer) all or any combination of elimination and choice expressing reality (ELECTRE) Tri-B parameters. For example, a decision maker can maintain the values for indifference, preference and veto thresholds, and the study’s algorithm can find the criteria weights, reference profiles and the lambda cutting level. The study’s approach is inspired by a machine learning ensemble technique, the random forest, and for that, the authors named the study’s approach as ELECTRE tree algorithm.Design/methodology/approachFirst, the authors generate a set of ELECTRE Tri-B models, where each model solves a random sample of criteria and alternates. Each sample is made with replacement, having at least two criteria and between 10% and 25% of alternates. Each model has its parameters optimized by a genetic algorithm (GA) that can use an ordered cluster or an assignment example as a reference to the optimization. Finally, after the optimization phase, two procedures can be performed; the first one will merge all models, finding in this way the elicitated parameters and in the second procedure, each alternate is classified (voted) by each separated model, and the majority vote decides the final class.FindingsThe authors have noted that concerning the voting procedure, nonlinear decision boundaries are generated and they can be suitable in analyzing problems of the same nature. In contrast, the merged model generates linear decision boundaries.Originality/valueThe elicitation of ELECTRE Tri-B parameters is made by an ensemble technique that is composed of a set of multicriteria models that are engaged in generating robust solutions.

A machine learning approach-based power theft detection using GRF optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
A. Prakash ◽  
A. Shyam Joseph ◽  
R. Shanmugasundaram ◽  
C.S. Ravichandran
Keyword(s):  
Machine Learning ◽  
Transmission Lines ◽  
High Speed ◽  
Design Methodology ◽  
Learning Approach ◽  
Smart Meter ◽  
Financial Loss ◽  
Content Type ◽  
Garra Rufa ◽  
Machine Learning Approach

Purpose This paper aims to propose a machine learning approach-based power theft detection using Garra Rufa Fish (GRF) optimization. Here, the analyzing of power theft is an important part to reduce the financial loss and protect the electricity from fraudulent users. Design/methodology/approach In this section, a new method is implemented to reduce the power theft in transmission lines and utility grids. The detection of power theft using smart meter with reliable manner can be achieved by the help of GRF algorithm. Findings The loss of power due to non-technical loss is small by using this proposed algorithm. It provides some benefits like increased predicting capacity, less complexity, high speed and high reliable output. The result is analyzed using MATLAB/Simulink platform. The result is compared with an existing method. According to the comparison result, the proposed method provides the good performance than existing method. Originality/value The proposed method gives good results of comparison than those of the other techniques and has an ability to overcome the associated problems.

An algorithm to elicitate ELECTRE II, III and IV parameters

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Brunno e Souza Rodrigues ◽  
Carla Martins Floriano ◽  
Valdecy Pereira ◽  
Marcos Costa Roboredo
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Design Methodology ◽  
Content Type ◽  
Numerical Examples ◽  
Ensemble Technique ◽  
Robust Solutions ◽  
Ranking Trees ◽  
Final Rank ◽  
First Time

PurposeThis paper presents an algorithm that can elicitate all or any combination of parameters for the ELECTRE II, III or IV, methods. The algorithm takes some steps of a machine learning ensemble technique, the random forest, and for that, the authors named the approach as Ranking Trees Algorithm.Design/methodology/approachFirst, for a given method, the authors generate a set of ELECTRE models, where each model solves a random sample of criteria and actions (alternatives). Second, for each generated model, all actions are projected in a 1D space; in general, the best actions have higher values in a 1D space than the worst ones; therefore, they can be used to guide the genetic algorithm in the final step, the optimization phase. Finally, in the optimization phase, each model has its parameters optimized.FindingsThe results can be used in two different ways; the authors can merge all models, to find the elicitated parameters in this way, or the authors can ensemble the models, and the median of all ranks represents the final rank. The numerical examples achieved a Kendall Tau correlation rank over 0.85, and these results could perform as well as the results obtained by a group of specialists.Originality/valueFor the first time, the elicitation of ELECTRE parameters is made by an ensemble technique composed of a set of uncorrelated multicriteria models that can generate robust solutions.

Detection and evaluation of heating load of building by machine learning

Sensor Review ◽  
2018 ◽  
Vol 38 (1) ◽  
pp. 99-101 ◽  
Author(s):  
Khaled Mohamed Himair Swhli ◽  
Srdjan Jovic ◽  
Nebojša Arsic ◽  
Petar Spalevic
Keyword(s):  
Machine Learning ◽  
Design Methodology ◽  
Diffuse Radiation ◽  
Dew Point ◽  
Learning Approach ◽  
Nonlinear Phenomenon ◽  
Content Type ◽  
Machine Learning Approach ◽  
Heating Load ◽  
Temperature Radiation

Purpose This paper aims to explore detection of heating load of building by machine learning. Detection of heating load of building is very important in design of buildings due to efficient energy consumption. Design/methodology/approach In this study, detection of heating load of building based on effects of dry-bulb temperature, dew-point temperature, radiation, diffuse radiation and wind speed was analyzed. Machine learning approach was implemented for such a purpose. Findings The obtained results could be useful for future planning of heating load of buildings. Because the heating load of building is a very nonlinear phenomenon, it is suitable to use machine learning approach to avoid the nonlinearity of the system. Originality/value The obtained results could be used effectively in detection of heating load of buildings.

Feasibility study of automatically performing the concrete delivery dispatching through machine learning techniques

2015 ◽  
Vol 22 (5) ◽  
pp. 573-590 ◽  
Author(s):  
Mojtaba Maghrebi ◽  
Claude Sammut ◽  
S. Travis Waller
Keyword(s):  
Machine Learning ◽  
Human Resources ◽  
Design Methodology ◽  
Machine Learning Techniques ◽  
Mixing Process ◽  
Content Type ◽  
Practical Solution ◽  
Learning Techniques ◽  
Ready Mixed Concrete ◽  
Practical Implications

Purpose – The purpose of this paper is to study the implementation of machine learning (ML) techniques in order to automatically measure the feasibility of performing ready mixed concrete (RMC) dispatching jobs. Design/methodology/approach – Six ML techniques were selected and tested on data that was extracted from a developed simulation model and answered by a human expert. Findings – The results show that the performance of most of selected algorithms were the same and achieved an accuracy of around 80 per cent in terms of accuracy for the examined cases. Practical implications – This approach can be applied in practice to match experts’ decisions. Originality/value – In this paper the feasibility of handling complex concrete delivery problems by ML techniques is studied. Currently, most of the concrete mixing process is done by machines. However, RMC dispatching still relies on human resources to complete many tasks. In this paper the authors are addressing to reconstruct experts’ decisions as only practical solution.

scholarly journals Text mining analysis roadmap (TMAR) for service research

2020 ◽  
Vol 34 (1) ◽  
pp. 30-47 ◽  
Author(s):  
Mohamed Zaki ◽  
Janet R. McColl-Kennedy
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Design Methodology ◽  
Data Sources ◽  
Theory Testing ◽  
Service Research ◽  
Data Types ◽  
Content Type ◽  
Research And Practice ◽  
Mining Methods

Purpose The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts. Design/methodology/approach The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts. Findings At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice. Originality/value There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.

MMSPhiD: a phoneme based phishing verification model for persons with visual impairments

2018 ◽  
Vol 26 (5) ◽  
pp. 613-636 ◽  
Author(s):  
Gunikhan Sonowal ◽  
KS Kuppusamy
Keyword(s):  
Machine Learning ◽  
Design Methodology ◽  
Practical Implication ◽  
Visual Impairments ◽  
Similarity Metrics ◽  
Content Type ◽  
Proposed Model ◽  
Verification Model ◽  
Phishing Detection ◽  
Metrics Model

Purpose This paper aims to propose a model entitled MMSPhiD (multidimensional similarity metrics model for screen reader user to phishing detection) that amalgamates multiple approaches to detect phishing URLs. Design/methodology/approach The model consists of three major components: machine learning-based approach, typosquatting-based approach and phoneme-based approach. The major objectives of the proposed model are detecting phishing URL, typosquatting and phoneme-based domain and suggesting the legitimate domain which is targeted by attackers. Findings The result of the experiment shows that the MMSPhiD model can successfully detect phishing with 99.03 per cent accuracy. In addition, this paper has analyzed 20 leading domains from Alexa and identified 1,861 registered typosquatting and 543 phoneme-based domains. Research limitations/implications The proposed model has used machine learning with the list-based approach. Building and maintaining the list shall be a limitation. Practical implication The results of the experiments demonstrate that the model achieved higher performance due to the incorporation of multi-dimensional filters. Social implications In addition, this paper has incorporated the accessibility needs of persons with visual impairments and provides an accessible anti-phishing approach. Originality/value This paper assists persons with visual impairments on detection phoneme-based phishing domains.

scholarly journals Supervised learning and resampling techniques on DISC personality classification using Twitter information in Bahasa Indonesia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ema Utami ◽  
Irwan Oyong ◽  
Suwanto Raharjo ◽  
Anggit Dwi Hartanto ◽  
Sumarni Adi
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Design Methodology ◽  
Sampling Technique ◽  
Profile Data ◽  
Content Type ◽  
Robust Model ◽  
Personality Classification ◽  
Bahasa Indonesia

PurposeGathering knowledge regarding personality traits has long been the interest of academics and researchers in the fields of psychology and in computer science. Analyzing profile data from personal social media accounts reduces data collection time, as this method does not require users to fill any questionnaires. A pure natural language processing (NLP) approach can give decent results, and its reliability can be improved by combining it with machine learning (as shown by previous studies).Design/methodology/approachIn this, cleaning the dataset and extracting relevant potential features “as assessed by psychological experts” are essential, as Indonesians tend to mix formal words, non-formal words, slang and abbreviations when writing social media posts. For this article, raw data were derived from a predefined dominance, influence, stability and conscientious (DISC) quiz website, returning 316,967 tweets from 1,244 Twitter accounts “filtered to include only personal and Indonesian-language accounts”. Using a combination of NLP techniques and machine learning, the authors aim to develop a better approach and more robust model, especially for the Indonesian language.FindingsThe authors find that employing a SMOTETomek re-sampling technique and hyperparameter tuning boosts the model’s performance on formalized datasets by 57% (as measured through the F1-score).Originality/valueThe process of cleaning dataset and extracting relevant potential features assessed by psychological experts from it are essential because Indonesian people tend to mix formal words, non-formal words, slang words and abbreviations when writing tweets. Organic data derived from a predefined DISC quiz website resulting 1244 records of Twitter accounts and 316.967 tweets.

Evaluating a programming topic using GitHub data: what we can learn about machine learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paolo Dello Vicario ◽  
Valentina Tortolini
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Programming Languages ◽  
Design Methodology ◽  
Social Impact ◽  
Content Type ◽  
Collaborative Structure

Purpose The purpose of this paper is to define a methodology to analyze links between programming topics and libraries starting from GitHub data. Design/methodology/approach This paper developed an analysis over machine learning repositories on GitHub, finding communities of repositories and studying the anatomy of collaboration around a popular topic such as machine learning. Findings This analysis indicates the significant importance of programming languages and technologies such as Python and Jupyter Notebook. It also shows the rise of deep learning and of specific libraries such as Tensorflow from Google. Originality/value There exists no survey or analysis based on how developers influence each other for specific topics. Other researchers focused their analysis on the collaborative structure and social impact instead of topic impact. Using this methodology to analyze programming topics is important not just for machine learning but also for other topics.

scholarly journals A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine-Learning Approach

Forecasting ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 267-283
Author(s):  
Alireza Rezazadeh
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Optimal Decision ◽  
Computing Platform ◽  
Machine Learning Approach ◽  
Successful Business ◽  
Decision Boundaries ◽  
Sales Decision ◽  
Business To Business ◽  
Core Part

Predicting the outcome of sales opportunities is a core part of successful business management. Conventionally, undertaking this prediction has relied mostly on subjective human evaluations in the process of sales decision-making. In this paper, we addressed the problem of forecasting the outcome of Business to Business (B2B) sales by proposing a thorough data-driven Machine-Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure Machine-Learning Service (Azure ML). This workflow consists of two pipelines: (1) An ML pipeline to train probabilistic predictive models on the historical sales opportunities data. In this pipeline, data is enriched with an extensive feature enhancement step and then used to train an ensemble of ML classification models in parallel. (2) A prediction pipeline to use the trained ML model and infer the likelihood of winning new sales opportunities along with calculating optimal decision boundaries. The effectiveness of the proposed workflow was evaluated on a real sales dataset of a major global B2B consulting firm. Our results implied that decision-making based on the ML predictions is more accurate and brings a higher monetary value.

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