Linear regression combined KNN algorithm to identify latent defects for imbalance data of ICs

Huang, Liang; Song, Tai; Jiang, Tiezhen

doi:10.1016/j.mejo.2022.105641

Cited by 22 publications

(9 citation statements)

References 17 publications

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“…The KNN algorithm works by first defining a distance metric between the input data points in the feature space. The most common distance metric is the Euclidean distance, but other metrics such as the Manhattan distance and cosine distance can also be used [69]. KNN has a few hyperparameters that can be tuned to achieve optimal performance on a given dataset.…”

Section: ) K-nearest Neighbormentioning

confidence: 99%

Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation

Rahman,

Sarwar,

Kader

et al. 2024

IEEE Access

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The average software company spends a huge amount of its revenue on R&D for how to deliver software on time. Accurate software effort estimation is critical for successful project planning, resource allocation, and on-time delivery within budget for sustainable software development. However, both overestimation and underestimation pose significant challenges in software development, necessitating continuous improvement in estimation techniques. This study reviews recent machine learning approaches exploited to enhance software effort estimation (SEE) accuracy, focusing on research published between 2020 and 2023. The literature review employed an approach to identify pertinent research on machine learning techniques for software estimation efforts. Additionally, comparative experiments were conducted employing five commonly used ML methods: K-Nearest Neighbor, Support Vector Machine, Random Forest, Logistic Regression, and LASSO Regression. These techniques were assessed using five widely employed accuracy metrics such as Mean Squared Error (MSE), Mean Magnitude of Relative Error (MMRE), R-squared, Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) on seven benchmark datasets (Albrecht, Desharnais, China, Kemerer, Mayazaki94, Maxwell, COCOMO). By carefully reviewing study quality, analyzing results across the literature, and rigorously evaluating experimental outcomes, clear conclusions were drawn about the most promising techniques for achieving state-of-the-art accuracy in estimating software effort. This study makes three key contributions to the field: firstly, it furnishes a thorough overview of recent machine learning research in software effort estimation (SEE); secondly, it provides data-driven guidance for researchers and practitioners to select optimal methods for accurate effort estimation; and thirdly, it demonstrates the performance of publicly available datasets through experimental analysis. Enhanced estimation supports the development of better predictive models for software project time, cost, and staffing needs. The findings aim to focus future research directions and tool development toward the most accurate machine learning approaches for modeling software development effort, costs, and delivery schedules.

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Section: ) K-nearest Neighbormentioning

confidence: 99%

Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation

Rahman,

Sarwar,

Kader

et al. 2024

IEEE Access

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“…The k-NN algorithm is a supervised ML model, which uses non-parametric vectors to determine an unknow point [109]. However, given that it is also based on the distance between points distributed on a possible multi-dimensional space, a distance metric must be implemented, often the Euclidean distance [109][110][111]. The k-NN architecture can be applied to data classification and regression model cases [109,112].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

Oliveira Santos,

Guimarães,

Lima Neto

et al. 2024

Preprint

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Eutrophication, a global concern, impacts water quality, ecosystems, and human health. It’s crucial to monitor algal blooms in freshwater reservoirs, as they indicate the trophic condition of a waterbody through Chlorophyll-a (Chla) concentration. Traditional monitoring methods, however, are expen-sive and time-consuming. Addressing this hindrance, we developed models using remotely sensed data from the Sentinel-2 satellite for large-scale coverage, including its bands and spectral indexes, to estimate the Chla concentration on 149 freshwater reservoirs in Ceará, Brazil. Several machine learning models were trained and tested, including k-nearest neighbours, random forests, extreme gradient boosting, the least absolute shrinkage, group method of data handling (GMDH), and sup-port vector machine models. A stepwise approach determined the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH, achieving an R2 of 0.91, MAPE of 102.34%, and RMSE of 20.38 g/L, which are values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near infra-red bands.

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“…Consequently, the KNN classification algorithm can be employed to automatically fill in default data. The flow chart illustrating the process of default value filling is depicted in Figure 2 [23,24].…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Optimization of Abnormal Hydraulic Fracturing Conditions of Unconventional Natural Gas Reservoirs Based on a Surrogate Model

Yang,

Han,

Liu

et al. 2024

Processes

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Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based method for optimizing abnormal conditions during hydraulic fracturing of unconventional natural gas reservoirs. Firstly, the main controlling factors of abnormal conditions are selected through a hybrid controlling analysis, upon which a surrogate model is established for predicting the occurrence probability of abnormal conditions, rather than whether abnormal conditions happen or not. Subsequently, a machine learning-based optimization algorithm is developed to minimize the occurrence probability of abnormal conditions, acknowledging their inevitability during the fracturing process. The optimal results demonstrate the proposed method outperforms traditional methods, on average. The proposed methodology is more in line with the needs of practical operation in an environment full of uncertainty.

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Linear regression combined KNN algorithm to identify latent defects for imbalance data of ICs

Cited by 22 publications

References 17 publications

Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation

Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

Optimization of Abnormal Hydraulic Fracturing Conditions of Unconventional Natural Gas Reservoirs Based on a Surrogate Model

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