scholarly journals Selected Robust Logistic Regression Specification for Classification of Multi‑dimensional Functional Data in Presence of Outlier

2018 ◽  
Vol 2 (334) ◽  
Author(s):  
Mirosław Krzyśko ◽  
Łukasz Smaga

In this paper, the binary classification problem of multi‑dimensional functional data is considered. To solve this problem a regression technique based on functional logistic regression model is used. This model is re‑expressed as a particular logistic regression model by using the basis expansions of functional coefficients and explanatory variables. Based on re‑expressed model, a classification rule is proposed. To handle with outlying observations, robust methods of estimation of unknown parameters are also considered. Numerical experiments suggest that the proposed methods may behave satisfactory in practice.

2009 ◽  
Vol 28 (30) ◽  
pp. 3798-3810 ◽  
Author(s):  
Jian Huang ◽  
Agus Salim ◽  
Kaibin Lei ◽  
Kathleen O'Sullivan ◽  
Yudi Pawitan

2014 ◽  
Vol 543-547 ◽  
pp. 2724-2727
Author(s):  
Liu Yang ◽  
Jiang Yan Dai ◽  
Miao Qi ◽  
Qing Ji Guan

We present a novel moving shadow detection method using logistic regression in this paper. First, several types of features are extracted from pixels in foreground images. Second, the logistic regression model is constructed by random pixels selected from video frames. Finally, for a new frame in one video, we take advantage of the constructed regression model to implement the classification of moving shadows and objects. To verify the performance of the proposed method, we test it on several different surveillance scenes and compare it with some well-known methods. Extensive experimental results indicate that the proposed method not only can separate moving shadows from moving objects accurately, but also is superior to several existing methods.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiao-Ying Liu ◽  
Sheng-Bing Wu ◽  
Wen-Quan Zeng ◽  
Zhan-Jiang Yuan ◽  
Hong-Bo Xu

AbstractBiomarker selection and cancer classification play an important role in knowledge discovery using genomic data. Successful identification of gene biomarkers and biological pathways can significantly improve the accuracy of diagnosis and help machine learning models have better performance on classification of different types of cancer. In this paper, we proposed a LogSum + L2 penalized logistic regression model, and furthermore used a coordinate decent algorithm to solve it. The results of simulations and real experiments indicate that the proposed method is highly competitive among several state-of-the-art methods. Our proposed model achieves the excellent performance in group feature selection and classification problems.


2020 ◽  
Author(s):  
Juan José Hidalgo ◽  
Antoni Llueca ◽  
Irene Zolfaroli ◽  
Nadia Veiga ◽  
Ester Ortiz ◽  
...  

Aims: To compare the diagnostic performance of two ultrasound-based diagnostic systems for the classification of benign or malignant adnexal masses, the three-step strategy and the predictive logistic regression model LR2, both proposed by the International Ovarian Tumour Analysis (IOTA) Group. Material and methods: Prospective observational study at a single centre that included patients diagnosed with a persistent adnexal mass by transvaginal ultrasound over a period of two years. They were evaluated by a non-expert sonographer by applying the three-step diagnostic strategy and the LR2 predictive model to classify the masses as benign or malignant. Patients were treated surgically or followed up for at least one year, taking as the standard reference for benignity or malignancy the histological diagnosis of the lesion or ultrasound changes suggestive of malignancy during the follow-up period. Sensitivity, specificity, positive and negative likelihood ratios and overall accuracy of both systems was calculated and compared. Results: One hundred patients were included, with a mean age of 50.6 years (range 18-87). Surgery was performed on 62 (62%) patients and 38 (38%) were managed expectantly. Eighty-three (83%) lesions were benign and 17 (17%) were malignant. The IOTA three-step strategy presented sensitivity of 94.1% (95%CI, 86.7-98.3%) and specificity 97.6% (95%CI, 94.8-99%). The LR2 logistic regression model showed sensitivity 94.1% (95%CI, 73-98.9%) and specificity 81.9% (95%CI 72.3-88.7%). Comparison of the two systems showed a statistically significant dif-ference in specificity in favour of the three-step strategy. Conclusions: The IOTA three-step strategy, in addition to being sim-ple to use in clinical practice, has a high diagnostic accuracy for the classification of benignity and malignancy of the adnexal masses, overtaking that of other predictive models such as the LR2 logistic regression model.


Author(s):  
N. A. M. R. Senaviratna ◽  
T. M. J. A. Cooray

One of the key problems arises in binary logistic regression model is that explanatory variables being considered for the logistic regression model are highly correlated among themselves. Multicollinearity will cause unstable estimates and inaccurate variances that affects confidence intervals and hypothesis tests. Aim of this was to discuss some diagnostic measurements to detect multicollinearity namely tolerance, Variance Inflation Factor (VIF), condition index and variance proportions. The adapted diagnostics are illustrated with data based on a study of road accidents. Secondary data used from 2014 to 2016 in this study were acquired from the Traffic Police headquarters, Colombo in Sri Lanka. The response variable is accident severity that consists of two levels particularly grievous and non-grievous. Multicolinearity is identified by correlation matrix, tolerance and VIF values and confirmed by condition index and variance proportions. The range of solutions available for logistic regression such as increasing sample size, dropping one of the correlated variables and combining variables into an index. It is safely concluded that without increasing sample size, to omit one of the correlated variables can reduce multicollinearity considerably.


2016 ◽  
Vol 12 (9) ◽  
pp. 6572-6575
Author(s):  
Denisa Salillari ◽  
Luela Prifti

Considering authorship attribution as a classification problem we attempt to estimate the probability to find the right author for each text under study. In this paper using R we first improve the simple model for six Albanian texts, (I) increasing number of texts and number of independent variables and then compare the results taken with them of the multinomial logistic regression (II). The model was applied on a set of one hundred texts of ten different authors. For all the authors under study the average correct predicted probability is 0.918. Analyzing data from different Albanian texts, results that about 40% of their letters consist of vowels. As conclusion comparing results taken with them of (II) multinomial logistic regression model for Albanian texts has more advantages than logistic regression model.


2021 ◽  
Vol 12 ◽  
Author(s):  
Osamah Alwalid ◽  
Xi Long ◽  
Mingfei Xie ◽  
Jiehua Yang ◽  
Chunyuan Cen ◽  
...  

Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture.Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms.Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89–0.95] and 0.86 [95% CI: 0.80–0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. −1.60 and 2.35 vs. −1.01 on training and test cohorts, respectively, p < 0.001).Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.


2021 ◽  
Author(s):  
Lishan Dong ◽  
Hailin Shen ◽  
Sheng Wang ◽  
Zhiyi Lei ◽  
Jiangong Zhang ◽  
...  

Abstract Background: To evaluate whether texture analysis of dark intraplacental bands on T2WI can provide a novel methodological viewpoint valuable in assessing the classification of placenta accreta spectrum disorders (PAS disorders).Methods: 174 participants with suspected PAS disorders were consecutively included in the study. Texture analysis was implemented on dark intraplacental bands on T2WI by MaZda software. The two steps of parameter selection and reduction led to a decrease of the parameter space dimensionality. The logistics regression models were constructed with texture parameters to evaluate the classification of PAS disorders.Results: Both run length nonuniformity (RLN) and grey level nonuniformity (Gle) of four directions showed significant differences between participants with placenta accreta, increta and percreta (P﹤0.05). The AUC and cut-off for logistic regression model of accreta vs increta were 0.75 (95% CI: 0.54, 0.90) and 6.72, respectively; corresponding values for logistic regression model of increta vs percreta were 0.81 (95% CI: 0.61, 0.93) and 10.92. The sensitivity and specificity for cut-off of 6.72 were 88.46% and 84.62%, respectively; corresponding values for cut-off of 10.92 were 92.59% and 85.71%.Conclusion: Texture analysis offered promise for more quantitative and objective assessment of PAS disorders than other image modalities. It may be a useful add-on to MRI in evaluating the classification of PAS disorders. Trial registration: Registration number: ChiCTR2000038604 and name of registry: Evaluation of diagnostic accuracy of MRI multi-parameter imaging combined with texture analysis for placenta accreta spectrum disorders (PAD).


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