Improving Failure Prediction by Ensembling the Decisions of Machine Learning Models: A Case Study

Campos, Joao R.; Costa, Ernesto; Vieira, Marco

doi:10.1109/access.2019.2958480

Cited by 13 publications

(4 citation statements)

References 29 publications

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“…These experiments were carried out on datasets provided by Eclipse, Mozilla, Firefox, NetBeans and Open office. In [22] the authors discuss about the growing complexity of the software and its influence on the quality of the product. There is no way that all the defects can be predicted beforehand using machine learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Framework for the Automation of SDLC Phases using Artificial Intelligence and Machine Learning Techniques

Shankar,

Chaudhari

2023

IJRITCC

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Software Engineering acts as a foundation stone for any software that is being built. It provides a common road-map for construction of software from any domain. Not following a well-defined Software Development Model have led to the failure of many software projects in the past. Agile is the Software Development Life Cycle (SDLC) Model that is widely used in practice in the IT industries to develop software on various technologies such as Big Data, Machine Learning, Artificial Intelligence, Deep learning. The focus on Software Engineering side in the recent years has been on trying to automate the various phases of SDLC namely- Requirements Analysis, Design, Coding, Testing and Operations and Maintenance. Incorporating latest trending technologies such as Machine Learning and Artificial Intelligence into various phases of SDLC, could facilitate for better execution of each of these phases. This in turn helps to cut-down costs, save time, improve the efficiency and reduce the manual effort required for each of these phases. The aim of this paper is to present a framework for the application of various Artificial Intelligence and Machine Learning techniques in the different phases of SDLC.

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Section: Related Workmentioning

confidence: 99%

Framework for the Automation of SDLC Phases using Artificial Intelligence and Machine Learning Techniques

Shankar,

Chaudhari

2023

IJRITCC

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“…Taking into consideration the comparative analysis from Table 2 for supervised machine learning classifiers, four machine learning classifiers viz. Random Forest (RF), Support Vector Machine(SVM), Naïve Bayes (NB) and Logistic regression(LR) [13][14][15][16][17] were used for supervised classification using Machine Learning [2,18,19] on the above datasets. The train test method with Stratified Crossfold with k=10 strategy was used for classifier experimentation.…”

Section: Datasets Usedmentioning

confidence: 99%

Healthcare Critical Diagnosis Accuracy

Pankaj Javale,

Desai

2023

Int. j. electr. comput. eng. syst. (Online)

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Since at least a decade, Machine Learning has attracted the interest of researchers. Among the topics of discussion is the application of Machine Learning (ML) and Deep Learning (DL) to the healthcare industry. Several implementations are performed on the medical dataset to verify its precision. The four main players, True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN), play a crucial role in determining the classifier's performance. Various metrics are provided based on the main players. Selecting the appropriate performance metric is a crucial step. In addition to TP and TN, FN should be given greater weight when a healthcare dataset is evaluated for disease diagnosis or detection. Thus, a suitable performance metric must be considered. In this paper, a novel machine learning metric referred to as Healthcare-Critical-Diagnostic-Accuracy (HCDA) is proposed and compared to the well-known metrics accuracy and ROC_AUC score. The machine learning classifiers Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB) are implemented on four distinct datasets. The obtained results indicate that the proposed HCDA metric is more sensitive to FN counts. The results show, that even if there is rise in %FN for dataset 1 to 10.31 % then too accuracy is 83% ad HCDA shows correlated drop to 72.70 %. Similarly, in dataset 2 if %FN rises to 14.80 for LR classifier, accuracy is 78.2 % and HCDA is 63.45 %. Similar kind of results are obtained for dataset 3 and 4 too. More FN counts result in a lower HCDA score, and vice versa. In common exiting metrics such as Accuracy and ROC_AUC score, even as the FN count increases, the score increases, which is misleading. As a result, it can be concluded that the proposed HCDA is a more robust and accurate metric for Critical Healthcare Analysis, as FN conditions for disease diagnosis and detection are taken into account more than TP and TN.

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“…Thus, we used an ensemble modeling approach for the caracals, as this method provides a far more accurate distribution range of rarely detected species (Guisan et al 1999;Pouteau et al 2012;Breiner et al 2015;Siders et al 2020;Xie et al 2021). Ensemble models avoid under-or over-prediction of niche estimates (Campos et al 2019), which in turn provides detailed and precise spatial information, and ultimately aids in increasing the detection probability of species by narrowing down the search area for the species.…”

Section: Conventional Species Distribution Modeling Vs Ensemble Model...mentioning

confidence: 99%

Hunting of hunted: an ensemble modeling approach to evaluate suitable habitats for caracals in India

et al. 2022

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Background Large-scale hunting and various anthropogenic pressures in the recent past have pushed the Asiatic caracal (Caracal caracal schmitzi), an elusive medium-sized and locally threatened felid species towards local extinction in India. Though widely distributed historically, it has been sparsely reported from several regions of central and northern states in India till twentieth century. Later, the species distribution became confined only to the states of Rajasthan, Gujarat and Madhya Pradesh, which have had reported sightings in the twenty-first century. In order to highlight the potentially suitable habitats for Asiatic caracals in India, we targeted forth-filtering of the spatial model ensemble by creating and utilizing the validated and spatially thinned species presence information (n = 69) and related ecological variables (aridity, NDVI, precipitation seasonality, temperature seasonality, terrain ruggedness), filtered with anthropological variable (nightlight). Results Out of eight spatial prediction models, the two most parsimonious models, Random Forest (AUC 0.91) and MaxEnt (AUC 0.89) were weighted and ensembled. The ensemble model indicated several clustered habitats, covering 1207.83 km2 areas in Kachchh (Gujarat), Aravalli mountains (Rajasthan), Malwa plateau (Rajasthan and Madhya Pradesh), and Bundelkhand region (Madhya Pradesh) as potentially suitable habitats for caracals. Output probabilities of pixels were further regressed with converted vegetation height data within selected highly potential habitats, i.e., Ranthambore Kuno Landscape (RKL) (suitability ~ 0.44 + 0.03(vegetation height) **, R2 = 0.27). The regression model inferred a significant positive relation between vegetation height and habitat suitability, hence the lowest ordinal class out of three classes of converted vegetation height was masked out from the RKL, which yielded in an area of 567 km2 as potentially highly suitable habitats for caracals, which can be further proposed as survey areas and conservation priority areas for caracals. Conclusion The study charts out the small pockets of landscape in and around dryland protected areas, suitable for caracal in the Indian context, which need attention for landscape conservation.

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Improving Failure Prediction by Ensembling the Decisions of Machine Learning Models: A Case Study

Cited by 13 publications

References 29 publications

Framework for the Automation of SDLC Phases using Artificial Intelligence and Machine Learning Techniques

Framework for the Automation of SDLC Phases using Artificial Intelligence and Machine Learning Techniques

Healthcare Critical Diagnosis Accuracy

Hunting of hunted: an ensemble modeling approach to evaluate suitable habitats for caracals in India

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