An empirical study on performances of multilayer perceptron, logistic regression, ANFIS, KNN and bagging CART

Ömürlü, İmran Kurt; Cantas, Fulden; Türe, Mevlüt; Öztürk, Hakan

doi:10.1080/09720510.2019.1696924

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…This ML algorithm is a feed-forward model [58], in which learning is through two phases: forward and backward [59]. Given the loss function E(x, y, θ), with x the inputs, θ parameters, y the target, and w ij h as the weight of the connection from neuron i of layer h-1 to neuron j, the MLP algorithm uses gradient descent theory or its variants to adjust weights by applying partial derivation of the loss function regard to each parameter [60] to get a new weight, as follows:…”

Section: Yearmentioning

confidence: 99%

Applying Data Mining in Graduates’ Employability

2023

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Envisaging an adequate IT/IS solution that can mitigate the employability problems is imperative because nowadays there is a high rate of unemployed graduates. Thus, the main goal of this systematic literature review (SLR) was to explore the application of data mining techniques in modeling employability and see how those techniques have been applied and which factors/variables have been retained to be the most predictors or/and prescribers of employability. Data mining techniques have shown the ability to serve as decision support tools in predicting and even prescribing employability. The review determined and analyzed the machine learning algorithms used in data mining to either predict or prescribe employability. This review used the PRISMA method to determine which studies from the existing literature to include as items for this SLR. Hence, 20 relevant studies, 16 of which are predicting employability and 4 of which are prescribing employability. These studies were selected from reliable databases: ScienceDirect, Springer, Wiley, IEEE Xplore, and Taylor and Francis. According to the results of this study, various data mining techniques can be used to predict and/or to prescribe employability. Furthermore, the variables/factors that predict and prescribe employability vary by country and the type of prediction or prescription conducted research. Nevertheless, all previous studies have relied more on skill as the main factor that predict and/or prescribe employability in developed countries and none studies have been conducted in unstable developing countries. Therefore, the need to conduct research on predicting or prescribing employability in such countries by trying to use contextual factors beyond skill as features.

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Section: Yearmentioning

confidence: 99%

Applying Data Mining in Graduates’ Employability

2023

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“…MLP is a feedforward model [35] which learns in two phases: forward and backward [36]. Given the loss function E(x, y, θ), with x the inputs, θ parameters, y the target, and w h ij as the weight of the connection from neuron i of layer h − 1 to neuron j, the MLP algorithm uses gradient descent theory or its variants to adjust weights by applying partial derivation of the loss function for each parameter [37] to get the new weight as follows:…”

Section: Deep Neural Networkmentioning

confidence: 99%

Predicting Employability of Congolese Information Technology Graduates Using Contextual Factors: Towards Sustainable Employability

Mpia¹,

Mwendia

Mburu

2022

Sustainability

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Predicting employability in an unstable developing country requires the use of contextual factors as predictors and a suitable machine learning model capable of generalization. This study has discovered that parental financial stability, sociopolitical, relationship, academic, and strategic factors are the factors that can contextually predict the employability of information technology (IT) graduates in the democratic republic of Congo (DRC). A deep stacking predictive model was constructed using five different multilayer perceptron (MLP) sub models. The deep stacking model measured good performance (80% accuracy, 0.81 precision, 0.80 recall, 0.77 f1-score). All the individual models could not reach these performances with all the evaluation metrics used. Therefore, deep stacking was revealed to be the most suitable method for building a generalizable model to predict employability of IT graduates in the DRC. The authors estimate that the discovery of these contextual factors that predict IT graduates’ employability will help the DRC and other similar governments to develop strategies that mitigate unemployment, an important milestone to achievement of target 8.6 of the sustainable development goals.

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“…It is a powerful ensemble algorithm that works well with small datasets. Bagging is used to reduce the variance in a trained model and prevents overfitting of data while training [45]. In this algorithm, the dataset is split into clusters.…”

Section: Logitboostmentioning

confidence: 99%

Novel Based Ensemble Machine Learning Classifiers for Detecting Breast Cancer

Srinivas

Madhusudhan

Dhanraj

et al. 2022

Mathematical Problems in Engineering

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Nowadays, for many industries, innovation revolves around two technological improvements, Artificial Intelligence (AI) and machine learning (ML). ML, a subset of AI, is the science of designing and applying algorithms that can learn and work on any activity from past experiences. Of all the innovations in the field of ML models, the most significant ones have turned out to be in medicine and healthcare, since it has assisted doctors in the treatment of different types of diseases. Among them, early detection of breast cancer using ML algorithms has piqued the interest of researchers in this area. Hence, in this work, 20 ML classifiers are discussed and implemented in Wisconsin’s Breast Cancer dataset to classify breast cancer as malignant or benign. Out of 20, 9 algorithms are coded using Python in Colab notebooks and the remaining are executed using the Waikato Environment for Knowledge Analysis (WEKA) software. Among all, the stochastic gradient descent algorithm was found to yield the highest accuracy of 98%. The algorithms that gave the best results have been considered in the development of a novel ensemble model and the same was implemented in both WEKA and Python. The performance of the ensemble model in both platforms is compared based on metrics like accuracy, precision, recall, and sensitivity and investigated in detail. From this experimental comparative study, it was found that the ensemble model developed using Python has yielded an accuracy of 98.5% and that developed in the WEKA has yielded 97% accuracy.

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An empirical study on performances of multilayer perceptron, logistic regression, ANFIS, KNN and bagging CART

Cited by 7 publications

References 16 publications

Applying Data Mining in Graduates’ Employability

Applying Data Mining in Graduates’ Employability

Predicting Employability of Congolese Information Technology Graduates Using Contextual Factors: Towards Sustainable Employability

Novel Based Ensemble Machine Learning Classifiers for Detecting Breast Cancer

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