Discriminant Regression Research Articles

Perbandingan Analisis Diskriminan dan Analisis Regresi Logistik Ordinal dalam Prediksi Klasifikasi Kondisi Kesehatan Bank

The purpose of this research is to compare the accuracy of bank classification prediction based on Capital Adequacy Ratio (CAR), Earning Asset Quality (EAQ), Non Performing Loan (NPL), Return on Assets (ROA), Net Interest Margin (NIM), Short Term Mismatch (STM) and Loan to Deposit Ratio (LDR). Discriminant analysis and ordinal logistic regression analysis are compared in classifying the prediction. The data used are secondary data, namely data classification of bank conditions in Indonesia in 2014 obtained from research institute PT Infovesta Utama. Based on Apparent Error Rate (APER) score obtained, it can be said that discriminant analysis is better in predicting the classification of bank conditions in Indonesia than that of ordinal logistic regression analysis. Discriminant analysis has the average prediction accuracy of 80%, while ordinal logistic regression analysis has the average prediction accuracy of 74,38%.

The use of components’ weights improves the diagnostic accuracy of a health-related index

ABSTRACTThis work aims to evaluate whether the use of specific weights in each component of a health-related index, is associated with its diagnostic accuracy. In addition, the impact of a composite health-related index's components multitude is examined in relation to its classification ability. An un-weighted and various weighted indices were constructed using different weighting methods. The indices’ diagnostic ability was evaluated by using true positive rate, true negative rate, true rate, positive predictive value, negative predictive value and the area under the receiver operating characteristic curve. Weights used in this study were obtained from linear discriminant analysis and binary logistic regression. These indices were applied in both simulated and actual data; and a variety of scenarios was applied based on the distribution's parameters of the component variables and on the number of components used. Results indicate that weighted indices’ evaluation measures were significantly higher compared to the un-weighted one; whereas area under receiver operating characteristic curve was positively associated with the number of components of each index that were correlated with the outcome. Weighting of index's components, as well as greater number of components related to the investigated outcome should be recommended for the construction of accurate indices.

Clinical and Radiographic Correlation (According to CT) in Patients with Degenerative Lumbar Spinal Stenosis

Сomputed tomography (CT) is widely used in the diagnosis of degenerative pathology of the lumbar spine, but the relationship between clinical manifestations of lumbar stenosis and its anatomical prerequisites has not been sufficiently studied to date.The objective: to determine the significance of the morphometric parameters of lumbar stenosis according to CT scans and to establish their relationship with the prevailing symptoms of the disease.Material and methods. Seventy-five consecutive patients with clinically significant lumbar stenosis who underwent CT scan before surgery were enrolled in this study. The average values of thirteen different morphometric parameters were calculated at LIII–SI levels of the intervertebral discs and of the pedicels in the axial and sagittal views. The possibility of classification of clinical observations and the correlation of morphometric parameters with the clinical forms of lumbar stenosis were investigated using discriminant and logistic regression analysis. Results. CT scan with high probability allocates patients with predominant symptoms of neurogenic claudication or bilateral radiculopathy. The most significant morphometric predictors of this clinical group are the depth of the lateral recesses and the cross-sectional area of the spinal canal.Conclusion. CT scan significantly expands the informative value of magnetic resonance imaging and can be used in planning the decompressive stage of the surgery intervention in patients with lumbar spinal canal stenosis.

Robust Discriminant Regression for Feature Extraction.

Ridge regression (RR) and its extended versions are widely used as an effective feature extraction method in pattern recognition. However, the RR-based methods are sensitive to the variations of data and can learn only limited number of projections for feature extraction and recognition. To address these problems, we propose a new method called robust discriminant regression (RDR) for feature extraction. In order to enhance the robustness, the L2,1-norm is used as the basic metric in the proposed RDR. The designed robust objective function in regression form can be solved by an iterative algorithm containing an eigenfunction, through which the optimal orthogonal projections of RDR can be obtained by eigen decomposition. The convergence analysis and computational complexity are presented. In addition, we also explore the intrinsic connections and differences between the RDR and some previous methods. Experiments on some well-known databases show that RDR is superior to the classical and very recent proposed methods reported in the literature, no matter the L2-norm or the L2,1-norm-based regression methods. The code of this paper can be downloaded from http://www.scholat.com/laizhihui.

Conceiving Human Interaction by Visualising Depth Data of Head Pose Changes and Emotion Recognition via Facial Expressions

Affective computing in general and human activity and intention analysis in particular comprise a rapidly-growing field of research. Head pose and emotion changes present serious challenges when applied to player’s training and ludology experience in serious games, or analysis of customer satisfaction regarding broadcast and web services, or monitoring a driver’s attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests were selected in order to rapidly and accurately estimate head pose changes in an unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation (JSON)) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need to generate comprehensible visual representations from different sets of data, in this paper, we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor).

A ship recognition method of variational inference-based probability generative model using optical remote sensing image

Aiming at the requirements of effectively recognizing ships using optical remote sensing data, a ship recognition method of probability generative model based on variational inference is proposed. Firstly, according to the principle of region partition by pixel gray level, the feature extraction based on local spatial gray information of pixels is built, which can efficiently measure similarity degree between the current pixel and its neighborhood structure in a search window, and can be able to provide ship recognition feature stably and accurately. In addition, theoretical analysis shows that the feature extraction method has greatly reduced time complexity of the algorithm. Secondly, a classification method of probability generative model based on variational inference is presented, in this model, manifold similarity based on local reverse entropy operator is used to measure the discrepancy of samples, and the neighborhood samples are selected from top of defined dominant set. Finally, experimental results on real data demonstrate that the proposed method can be obtained a higher recognition performance than k-nearest-neighbor (KNN), support vector machine (SVM), hierarchical discriminant regression (HDR), probability generative model (PGM), dynamic probability generative model (DPGM), it satisfies the time efficiency requirements of ship recognition in projects.

Smoking and attitudes towards its cessation among native and international dental students in Lithuania

BackgroundDental professionals are uniquely positioned to discourage smoking among their patients. However, little is known about the role of cultural background and attitudes towards smoking in the education of these professionals. Our study aimed to compare native Lithuanian and international dental students’ smoking habits, knowledge about the harmfulness of smoking and attitudes towards smoking cessation.MethodsWe conducted a cross-sectional survey of smoking and its cessation among dental students at the Lithuanian University of Health Sciences (Kaunas, Lithuania) in 2012. All Lithuanian and international dental students in each year of dental school were invited to participate in the survey during a compulsory practical class or seminar. Altogether 606 students participated in the survey with a response rate of 84.2%. Explanatory factorial analysis (EFA), multivariate Discriminant Analysis (DA) and Binary Logistic Regression (BLR) served for the statistical analyses.ResultsThe percentages of occasional/current regular smokers were 41.1% and 55.7% (p = 0.068) among Lithuanian and international male students, and 22.7% and 22.9% (p = 0.776) among Lithuanian and international female students, respectively. The international dental students had a deeper knowledge of the harmfulness/addictiveness of smoking and held more positive attitudes towards smoking cessation among their patients than did the native Lithuanian dental students.ConclusionsThe findings of the study underscored the need to properly incorporate tobacco cessation training into the curriculum of dental education. However, consideration of the cultural background of dental students in building up their capacity and competence for intervening against smoking is essential.

Noisy label based discriminative least squares regression and its kernel extension for object identification

Noisy label based discriminative least squares regression and its kernel extension for object identification

A Gaussian copula regression model for movie box-office revenues prediction

In this article, we revisit the task of movie box-office revenues prediction using multi-type features. The movie box-office revenues are affected by numerous factors. Previous work with discriminative models assumes these factors are identically and independently distributed. The correlations between these factors are rarely considered, which limited the performances of discriminative models in this task. To address these problems, we investigate a novel Gaussian copula regression model. Based on this model, we do not need to make any prior assumptions about the marginal distributions of the features. In particular, we perform a cumulative probability estimation on each of the smoothed features. The estimation learns the marginal distributions and maps all features into a uniform vector space. Sequentially, we bridge the marginal distributions with a copula function to create their joint distribution, and learn the dependency structure between them. Moreover, we propose a computational-efficient approximate algorithm for responsible variable inference. Experimental results on two movie datasets from Chinese and U.S. market show that our approach outperforms strong discriminative regression baselines.

Comparison Between Discriminant Analysis Model and Logistic Regression Model to Predict the Distress of the Algerian Economic Companies

This study aims to select the appropriate method the most able to distinguish between distressed and non-distressed economic companies, which will lead us to know the most important variables that can detect the pre-distress for Algerian economic companies, specifically in the Algerian environment, through the comparison between the findings of the application of two of the most important statistical methods, i.e, discriminant and logistic analysis, on a sample of 60 companies; half of them is distressed and the other half is non-distressed according to four financial ratios selected among the financial ratios the most used by researchers, namely: sales to total assets, working capital to total assets, profit before interest and tax and the ratio of equity to total assets. We concluded, by comparing the findings of the discriminant model and logistic model classification, that the latter is more able to distinguish between distressed and non-distressed Algerian economic companies by 96.7%, compared to the findings of the Fischer discriminant model classification by 91.7%.

Perbandingan Antara Metode Discriminant Analysis Dan Logistic Regression Untuk Analisa Faktor-faktor Yang Mempengaruhi Prestasi Akademik Mahasiswa

The purpose of this empirical study conducted to determine the factors influencing student academic performance in Perbanas Institute Jakarta. Multiple Discriminant Analysis and Regression Logistic as main statistical multivariate analysis method were used in this study. Results of the analysis will be determined that the Logistic Regression method is more accurate and superior as well as determine the factors that influence students' academic performance Perbanas Institute. Student Academic Performance in the case study students of Perbanas Institute, using the comparison between discriminant analysis method and regression logistic were influenced by factors duration of study, student motivation, student family environmental factors, and Quality of Learning, and E-Learning System. This comparative study reveals that the logistic regression method is much better and more accurate than the Discriminant Analysis method.

A Supervised Learning Model for High-Dimensional and Large-Scale Data

We introduce a new supervised learning model using a discriminative regression approach. This new model estimates a regression vector to represent the similarity between a test example and training examples while seamlessly integrating the class information in the similarity estimation. This distinguishes our model from usual regression models and locally linear embedding approaches, rendering our method suitable for supervised learning problems in high-dimensional settings. Our model is easily extensible to account for nonlinear relationship and applicable to general data, including both high- and low-dimensional data. The objective function of the model is convex, for which two optimization algorithms are provided. These two optimization approaches induce two scalable solvers that are of mathematically provable, linear time complexity. Experimental results verify the effectiveness of the proposed method on various kinds of data. For example, our method shows comparable performance on low-dimensional data and superior performance on high-dimensional data to several widely used classifiers; also, the linear solvers obtain promising performance on large-scale classification.

English

Knowledge on performance of genotypes and interrelationships among traits is very important for sugarcane breeding programmes. The objectives of this study were to assess the phenotypic relationship among 49 sugarcane genotypes and the inter-relationships among traits considered. The cluster analysis demonstrated that the 49 sugarcane genotypes studied were clustered into nine groups and were highly different for Pol in juice, cane yield (tons ha-1m-1), number of tillers (ha-1), purity% and milleable stalk population (ha-1). The relationship among sugarcane genotypes was not dependent on geographic origin, suggesting that a high proportion of total genetic variation was retained within the groups of origin and active genetic ex-change was found between different origins. The principal component analysis indicated that cane yield, milleable stalk height and milleable stalk diameter were highly correlated with sugar yield while the correlation of quality traits with sugar yield was weak. In contrary, path and multiple regression analysis revealed that cane yield, recoverable sucrose percentage (%) and Pol contribute more to the variability of sugar yield; these are very important traits for high sugar yield that should be considered in sugarcane breeding programmes. Moreover, milleable stalk height and milleable stalk population via cane yield and Brix, Pol, purity and number of internodes via recoverable sucrose percentage had high indirect effects on sugar yield suggesting these traits should also be given consideration during selection for high sugar yield. Generally, similar and adequate information was generated following the use of cluster, principal component, linear discriminant, path coefficient and multiple regression analyses indicating the use of multivariate analyses was successful and results of the study were more substantial to give concrete recommendation. Key words: Clusters, genotypes, multiple regression, path coefficient, phenotypic correlation.

Robust discriminative regression for facial landmark localization under occlusion

Facial landmark localization or facial alignment is a crucial initial step in face analysis. The paper proposes a novel discriminative regression framework called Robust Discriminative Regression (RDR) for facial landmark localization. RDR framework consists of multiple partial feature regressors and a regression tree combination strategy. The proposed method copes with the partial facial landmarks invisible problem together with the optimization problem of multiple outputs combination. The RDR framework can be applied to both raw shape regression and model-based shape parameters regression. In model-based shape parameters regression we propose a two-level regression strategy, the first level is for rigid motion parameter regression and the second one is for non-rigid deformation parameter regression. Experiments on three widely used “face in-the-wild” databases (LFPW, COFW and IBUG) show that the proposed RDR outperforms other state-of-the-art facial landmark localization strategies in raw shape regression especially under partial occlusions or large pose variations. It also shows that the two-level regression strategy within RDR framework could achieve better performance than one-level parameters regression.

Adaptive linear discriminant regression classification for face recognition

Linear discriminant regression classification (LDRC) was presented recently in order to boost the effectiveness of linear regression classification (LRC). LDRC aims to find a subspace for LRC where LRC can achieve a high discrimination for classification. As a discriminant analysis algorithm, however, LDRC considers an equal importance of each training sample and ignores the different contributions of these samples to learn the discriminative feature subspace for classification. Motivated by the fact that some training samples are more effectual in learning the low-dimensional feature space than other samples, in this paper, we propose an adaptive linear discriminant regression classification (ALDRC) algorithm by taking special consideration of different contributions of the training samples. Specifically, ALDRC makes use of different weights to characterize the different contributions of the training samples and utilizes such weighting information to calculate the between-class and the within-class reconstruction errors, and then ALDRC seeks to find an optimal projection matrix that can maximize the ratio of the between-class reconstruction error over the within-class reconstruction error. Extensive experiments carried out on the AR, FERET and ORL face databases demonstrate the effectiveness of the proposed method.

Comparison of cranial sex determination by discriminant analysis and logistic regression.

Various methods have been proposed for estimating dimorphism. The objective of this study was to compare sex determination results from cranial measurements using discriminant analysis or logistic regression. The study sample comprised 130 individuals (70 males) of known sex, age, and cause of death from San José cemetery in Granada (Spain). Measurements of 19 neurocranial dimensions and 11 splanchnocranial dimensions were subjected to discriminant analysis and logistic regression, and the percentages of correct classification were compared between the sex functions obtained with each method. The discriminant capacity of the selected variables was evaluated with a cross-validation procedure. The percentage accuracy with discriminant analysis was 78.2% for the neurocranium (82.4% in females and 74.6% in males) and 73.7% for the splanchnocranium (79.6% in females and 68.8% in males). These percentages were higher with logistic regression analysis: 85.7% for the neurocranium (in both sexes) and 94.1% for the splanchnocranium (100% in females and 91.7% in males).

Islamic Versus Conventional Banking: An Insight into the Malaysian Dual Banking System

This research aims at examining to match the performance of both Malaysian Islamic and conventional banking through profitability, efficiency, solvency and liquidity and risk management ratios using independent t-test and discriminant regression models. Fifteen financial ratios are applied to examine the competitiveness of the both industries created on the financial data of ten Malaysian banks, five from both industries, over the period of six financial years (2009-2015). According to the independent t-test descriptive statistics, the result finds that conventional banks perform well than Islamic banks in the context profitability and efficacy ratios. Nevertheless, in terms of solvency and liquidity & risk management ratios Islamic banks outperform conventional banks operating in Malaysia. Further, it has been revealed by the disciriminant analysis that in general conventional banks execute well than Islamic banks operating in Malaysia when it comes to the profitability, solvency, efficiency and liquidity & risk management ratios.

디지털 운행기록에 근거한 시내버스 운전자의 사고발생 예측모형 개발

This study aims to develop a model by which city bus drivers who are likely to cause an accident can be figured out based on the information about their actual driving records. For this purpose, from the information about the actual driving records of the drivers who have caused an accident and those who have not caused any, significance variables related to traffic accidents are drawn, and the accuracy between models is compared for the classification models developed, applying a discriminant analysis and logistic regression analysis. In addition, the developed models are applied to the data on other drivers' driving records to verify the accuracy of the models. As a result of developing a model for the classification of drivers who are likely to cause an accident, when deceleration () and acceleration to the right () are simultaneously in action, this variable was drawn as the optimal factor variable of the classification of drivers who had caused an accident, and the prediction model by discriminant analysis classified drivers who had caused an accident at a rate up to 62.8%, and the prediction model by logistic regression analysis could classify those who had caused an accident at a rate up to 76.7%. In addition, as a result of the verification of model predictive power of the models showed an accuracy rate of 84.1%.

Comparison of Logistic Regression and Linear Discriminant Analyses of the Determinants of Financial Sustainability of Rural Banks in Ghana

Financial Sustainability is a primary issue for successful rural and community banks’ services. Establishing a system of sustained provision of modern financial services has, however, been challenging and most controversial. Several studies have been conducted on the determinants of sustainability of institutions in various countries. However, the levels of significance of the factors that influence financial sustainability of banks vary with studies. In addition, the results are mixed and empirical evidence regarding the determinants of rural and community banks’ sustainability is also missing. The objective of this study therefore was to develop a model which could be used to identify likely future rural and community banks that are non-sustainable. This study examined the determinants of financial sustainability of Rural and community banks using discriminant analysis (LDA) and logistic regression (LR) models.

The examination of the assessment and evaluation course achievements of university students based on discriminant and logistic regression analysis

In this study, it is intended to analyze pass/fail classification determined according to the assessment and evaluation course grades of third-year university students with logistic regression analysis and discriminant analysis. This research is a correlational survey in scan model. Appropriate sampling method of non-selection sampling methods was used in determining the research group. Research Group is constitute d by 410 third-year students of Faculty of education at Recep Tayyip Erdogan University in 2013-2014 academic year. In the process of data collection, assessment and evaluation success test developed by researchers and assessment and evaluation test developed by “personal information” form and researchers with validity and reliability analysis were used. The severity levels of the variables active in classification (pass-fail) in the model was determined logistic regression analysis. Hosmer and Lemeshow test result showed that the model-data fit is provided (p >. 05). In this case, the change that occurred in the fit of the model has been found to be significant. The total percentage of correct classification of the discriminant function groups were higher than the percentage of the logistics analysis.