<i>k</i>‐Nearest neighbors local linear regression for functional and missing data at random

Rachdi, Mustapha; Laksaci, Ali; Kaid, Zoulikha; Benchiha, Abbassia; Al-Awadhi, Fahimah

doi:10.1111/stan.12224

Cited by 15 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the k value is relatively small, the approximate error of KNN will be relatively small, and the prediction result is easily affected by the nearest neighbor, which leads to overfitting. At the same time, the value of k also has a range limitation on the other side, because when the value of k is larger, the nearest neighbor error of the prediction result will be larger, which will also lead to a large deviation between the prediction result and the actual result [11]. In this case, the model is prone to underfitting.…”

Section: Selection Of Training Parametersmentioning

confidence: 99%

Research on Breast Cancer Classification and Prediction Model Based on Principal Component Analysis and Machine Learning

Wang

2023

TNS

View full text Add to dashboard Cite

In recent years, breast cancer has become one of the biggest threats to women's health, accounting for the majority of cancer deaths among women. Because early treatment of breast cancer has a great effect on the recovery of breast cancer, the diagnosis of breast cancer is particularly important. Machine learning, as the most popular method, is also used for model construction in this field. This study is based on data from breast tumors, which contain 10 morphological features of breast tumor nucleus. In this study, homogenization, standardization and feature selection were used to process the data and KNN algorithm was used to construct the classification prediction model, with principal component analysis (PCA) used to optimize the model. Finally, the original 30 variables were reduced to 3 variables and the model parameters were adjusted in order to achieve the best model with the accuracy of 98.54%. The final model achieves the highest operating efficiency and accuracy. In this study, through the visualization of PCA and the comparison of different models, the classification effect of the final model can be the best. This model can be applied to the clinical diagnosis of breast cancer patients, which is helpful to the early treatment efficiency and greatly reduce the mortality of breast cancer patients

show abstract

Section: Selection Of Training Parametersmentioning

confidence: 99%

Research on Breast Cancer Classification and Prediction Model Based on Principal Component Analysis and Machine Learning

Wang

2023

TNS

View full text Add to dashboard Cite

show abstract

“…While [25] took into account the estimation of the regression function and established these asymptotic properties under the condition of stationarity and ergodicity with MAR response. On the other hand, [26] used the k nearest neighbors method (k-NN) combined with the local linear method to estimate the regression function with a low number of randomly missing values repones (MAR). For semi-parametric partially linear multivariate models with randomly missing responses, we can cite the work of [27,28] while [29] is the first to study the SFPLR model for i.i.d data with a MAR response, and their results generalize those obtained in [27].…”

Section: Of 21mentioning

confidence: 99%

Nonparametric Partial Linear Estimation for Spatial Functional Data with Missing At-Random

Benchikh,

Almanjahie,

Fetitah

et al. 2023

Preprint

View full text Add to dashboard Cite

The aim of this paper is to study a semi-functional partial linear regression model (SFPLR) for spatial data with responses missing at random. The estimators are constructed by the kernel method, and some asymptotic properties such as probability convergence rates of the nonparametric component and asymptotic distribution of the parametric and nonparametric components are established under certain conditions. Next, the performances and the superiority of these estimators are presented and examined using a study on simulated data and on real data by carrying out a comparison between our semi-functional partially linear model with MAR estimator (SFPLRM), the semi-functional partially linear model with the full-case estimator (SFPLRC) and the nonparametric functional model estimator with MAR (FNPM). The results show that the proposed estimators outperform existing estimators as the number of random missing data increases.

show abstract

“…In addition, the effectiveness of this method is verified by experiments. Rachdi et al proposed a Knn local linear regression algorithm [45]. It combines KNN and local linear estimation and inherits many advantages of these two methods.…”

Section: Neighbor Searchmentioning

confidence: 99%

KNN Classification with One-step Computation

Zhang¹,

Li²

2021

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

KNN classification is an improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck problem of applying KNN classification due to the complete search of K nearest neighbors. In this paper, a one-step computation is proposed to replace the lazy part of KNN classification. The one-step computation actually transforms the lazy part to a matrix computation as follows. Given a test data, training samples are first applied to fit the test data with the least squares loss function. And then, a relationship matrix is generated by weighting all training samples according to their influence on the test data. Finally, a group lasso is employed to perform sparse learning of the relationship matrix. In this way, setting K value and searching K nearest neighbors are both integrated to a unified computation. In addition, a new classification rule is proposed for improving the performance of one-step KNN classification. The proposed approach is experimentally evaluated, and demonstrated that the one-step KNN classification is efficient and promising.

show abstract

k‐Nearest neighbors local linear regression for functional and missing data at random

Cited by 15 publications

References 36 publications

Research on Breast Cancer Classification and Prediction Model Based on Principal Component Analysis and Machine Learning

Research on Breast Cancer Classification and Prediction Model Based on Principal Component Analysis and Machine Learning

Nonparametric Partial Linear Estimation for Spatial Functional Data with Missing At-Random

KNN Classification with One-step Computation

Contact Info

Product

Resources

About