Genetic Algorithm Optimization on Nave Bayes for Airline Customer Satisfaction Classification

Airline companies need to provide satisfactory service quality so that people do not switch to using other airlines. The way that can be used to determine customer satisfaction is to use data mining techniques. Currently, the website www.kaggle.com has provided Airline Passenger Satisfaction data consisting of 22 attributes, 1 label and 25976 instances which are included in the supervised learning data category. Based on several previous studies, the Naïve Bayes algorithm can provide better classification performance than other classification algorithms. Several studies also state that the use of Naive Bayes can be optimized using Genetic Algorithm (GA) to obtain better performance. The use of Genetic Algorithm for Nave Bayes optimization in classifying Airline Passenger Satisfaction data requires further research to ensure the performance of the given classification. This study aims to compare the use of the Naive Bayes algorithm for the classification of Airline Passenger Satisfaction with and without GA optimization. The data validation process used in this study is to use split validation to divide the dataset into 95% training data and 5% testing data. The test results show that the use of GA on Naive Bayes can improve the classification performance of Airline Passenger Satisfaction data in terms of accuracy and recall with an accuracy value of 85.99% and a recall of 87.91%

The Use of Recurrent Nets for the Prediction of e-Commerce Sales

The increase in e-commerce sales and profits has been a source of much anxiety over the years. Due to the advances in Internet technology, more and more people choose to shop online. Online retailers can improve customer satisfaction using sentiment analysis in comments and reviews to gain higher profits. This study used Recurrent Neural Networks (RNNs) to predict future sales from previous using the Kaggle dataset. A Bidirectional Long Short Term Memory (BLTSM) RNN was employed by tuning various hyperparameters to improve accuracy. The results showed that this BLTSM model of the RNN was quite accurate at predicting future sales performance

Applying Linear Programming in Business Decision Making: A Case of Profit Maximization of a Commercial Housing Development

The current research shows how to utilize the linear programming to perform a profit maximization on two economic cases and analyze the sensitivities of the obtained solutions to identify the key factors influencing the solution, which then can be used as the basis to decide the most profitable business. The case of the current study is identifying the most profitable project of two housing developments in Indonesia. The linear programming of the simplex method is applied to address the problem. The findings indicate that the linear programming solution and its sensitivity analysis effectively and efficiently inform and assist businessmen in identifying the most profitable project under the given constraints. In this case, the linear programming with its sensitivity analysis asserts that selecting a business activity based merely on the amount of its apparent profit may lead to an improper business decision. An example from the current study shows that although the second project yields a higher profit, the solution of the linear programming suggests that the first project is more relevance to its market demand and it needs less capital than that the second project. The higher profit obtained of the second project tends to be relative or fictitious since both projects result in the same return on investment (ROI). Moreover, the sensitivity analysis indicates that the optimization solution of the first project is more stable to change than that of the second project. Keywords: Profit Optimization, Return on Investment, Market Demand, Sensitivity Analysis, Simplex Method DOI: 10.7176/EJBM/11-19-06 Publication date:July 31st 201

Ensemble learning with dynamic weighting for response modeling in direct marketing

Response modeling, a key to successful direct marketing, has become increasingly prevalent in recent years. However, it practically suffers from the difficulty of class imbalance, i.e., the number of responding (target) customers is often much smaller than that of the non-responding customers. This issue would result in a response model that is biased to the majority class, leading to the low prediction accuracy on the responding customers. In this study, we develop an Ensemble Learning with Dynamic Weighting (ELDW) approach to address the above problem. The proposed ELDW includes two stages. In the first stage, all the minority class instances are combined with different majority class instances to form a number of training subsets, and a base classifiers is trained in each subset. In the second stage, the results of the base classifiers are dynamically integrated, in which two factors are considered. The first factor is the cross entropy of neighbors in each subset, and the second factor is the feature similarity to the minority class instances. In order to evaluate the performance of ELDW, we conduct experimental studies on 10 imbalanced benchmark datasets. The results show that compared with other state-of-the-art imbalance classification algorithms, ELDW achieves higher accuracy on the minority class. Last, we apply the ELDW to a direct marketing activity of an insurance company to identify the target customers under a limited budget

A spring search algorithm applied to engineering optimization problems

At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke’s law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke’s law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching–learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA’s usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering

Bruk av metoder innen forklarbar maskinlæring for kundefrafallsprediksjon

Kunstig intelligens baserte systemer blir stadig anvendt i beslutningstakingsprosesser. Det er ikke lett for mennesker å forstå de grunnleggende prinsippene bak hvordan disse systemene fungerer. Ved å anvende ulike metoder innen forklarbar maskinlæring er det mulig å få en bedre av forståelse av disse modellene. I denne oppgaven undersøkes det hvorvidt metoder innen forklarbar maskinlæring som SHAP, kan anvendes for å forstå hvilke faktorer som påvirker maskinlæringsmodeller for kundefrafallsprediksjon i telekommunikasjonsbransjen. Det blir undersøkt hvilke variabler som påvirker modellene på både lokalt og globalt nivå. Analysen viser at variablene som påvirker selve modellen, nødvendigvis ikke har like stor påvirkning på de individuelleprediksjonene.Artificial intelligence-based systems are constantly being used in decision-making processes.The concepts behind the systems are not always interpretable for humans. By using methods within explainable machine learning, it is possible to get a better understanding of these models. This thesis tries to explore whether methods within explainable machine learning such as SHAP can be used to understand which factors that influence machine learning models within customer churn prediction. The focus is to understand the feature importance on these models at both local and global level. The results shows that the features which influence the model, do not necessarily have the same impact on the individual predictions

Profit maximizing logistic model for customer churn prediction using genetic algorithms

To detect churners in a vast customer base, as is the case with telephone service providers, companies heavily rely on predictive churn models to remain competitive in a saturated market. In previous work, the expected maximum profit measure for customer churn (EMPC) has been proposed in order to determine the most profitable churn model. However, profit concerns are not directly integrated into the model construction. Therefore, we present a classifier, named ProfLogit, that maximizes the EMPC in the training step using a genetic algorithm, where ProfLogit’s interior model structure resembles a lasso-regularized logistic model. Additionally, we introduce threshold-independent recall and precision measures based on the expected profit maximizing fraction, which is derived from the EMPC framework. Our proposed technique aims to construct profitable churn models for retention campaigns to satisfy the business requirement of profit maximization. In a benchmark study with nine real-life data sets, ProfLogit exhibits the overall highest, out-of-sample EMPC performance as well as the overall best, profit-based precision and recall values. As a result of the lasso resemblance, ProfLogit also performs a profit-based feature selection in which features are selected that would otherwise be excluded with an accuracy-based measure, which is another noteworthy finding.status: publishe