scholarly journals On the binary classification problem in discriminant analysis using linear programming methods

2020 ◽  
Vol 30 (1) ◽  
Author(s):  
Michael O. Olusola ◽  
Sydney I. Onyeagu
Keyword(s):  
Linear Programming ◽  
Discriminant Analysis ◽  
Binary Classification ◽  
Classification Problem ◽  
Solution Technique ◽  
Phase Method ◽  
Bound Constraints ◽  
Two Phase ◽  
Linear Discriminant ◽  
Binary Classification Problem

This paper is centred on a binary classification problem in which it is desired to assign a new object with multivariate features to one of two distinct populations as based on historical sets of samples from two populations. A linear discriminant analysis framework has been proposed, called the minimised sum of deviations by proportion (MSDP) to model the binary classification problem. In the MSDP formulation, the sum of the proportion of exterior deviations is minimised subject to the group separation constraints, the normalisation constraint, the upper bound constraints on proportions of exterior deviations and the sign unrestriction vis-à-vis the non-negativity constraints. The two-phase method in linear programming is adopted as a solution technique to generate the discriminant function. The decision rule on group-membership prediction is constructed using the apparent error rate. The performance of the MSDP has been compared with some existing linear discriminant models using a previously published dataset on road casualties. The MSDP model was more promising and well suited for the imbalanced dataset on road casualties.

scholarly journals Inventory model for deteriorating items with negative exponential demand, probabilistic deterioration and fuzzy lead time under partial back logging

2020 ◽  
Vol 30 (2) ◽  
Author(s):  
Nabendu Sen ◽  
Sumit Saha
Keyword(s):  
Binary Classification ◽  
Classification Problem ◽  
Solution Technique ◽  
Phase Method ◽  
Bound Constraints ◽  
Two Phase ◽  
Linear Discriminant ◽  
Binary Classification Problem ◽  
Negative Exponential ◽  
Two Populations

This paper is centred on a binary classification problem in which it is desired to assign a new object with multivariate features to one of two distinct populations as based on historical sets of samples from two populations. A linear discriminant analysis framework has been proposed, called the minimised sum of deviations by proportion (MSDP) to model the binary classification problem. In the MSDP formulation, the sum of the proportion of exterior deviations is minimised subject to the group separation constraints, the normalisation constraint, the upper bound constraints on proportions of exterior deviations and the sign unrestriction vis-à-vis the non-negativity constraints. The two-phase method in linear programming is adopted as a solution technique to generate the discriminant function. The decision rule on group-membership prediction is constructed using the apparent error rate. The performance of the MSDP has been compared with some existing linear discriminant models using a previously published dataset on road casualties. The MSDP model was more promising and well suited for the imbalanced dataset on road casualties.

scholarly journals A SATELLITE IMAGE CLASSIFICATION APPROACH BY USING ONE DIMENSIONAL DISCRIMINANT ANALYSIS

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 429-435
Author(s):  
A. Saglam ◽  
N. A. Baykan
Keyword(s):  
Discriminant Analysis ◽  
Satellite Images ◽  
Spatial Information ◽  
Binary Classification ◽  
Satellite Image ◽  
Real Life ◽  
Classification Problem ◽  
Coordinate Space ◽  
Linear Discriminant ◽  
One Dimensional

<p><strong>Abstract.</strong> The classification problem in the image processing field is an important challenge, so that in the process image pixels are separated into previously determined classes according to their features. This process provides a meaningful knowledge about an area thanks to the satellite images. Satellite images are digital images obtained from a satellite vehicle by the way scanning the interest areas with some specified sensors. These sensors provide the specific radiometric and spatial information about the surface of the object. This information allows the researchers to obtain reliable classification results to be used to solve some real life problems such as object extraction, mapping, recognition, navigation and disaster management. Linear Discriminant Analysis (LDA) is a supervised method that reduces the dimensions of data in respect to the maximum discrimination of the elements of the data. This method also transfers the data to a new coordinate space in which the discriminant features of the classes are highest using the objection data provided manually. In this work, we consider the classes as if the satellite images have two classes; one is foreground and the other is background. The true classes such as roofs, roads, buildings, spaces and trees are treated sequentially as the foreground. The area outside the foreground class is treated as the background. The one dimensional reduced feature values of pixels, such that each value is reduced according to the binary classification of each class, are considered as membership values to the classes. In this way, each pixel has membership values for each of the classes. Finally, the pixels are classified according to the membership values. We used the ISPRS WG III/4 2D Semantic Labeling Benchmark (Vaihingen) images includes the ground truths and give the accuracy result values for each class.</p>

scholarly journals Confidence interval for micro-averaged F1 and macro-averaged F1 scores

2021 ◽  
Author(s):  
Kanae Takahashi ◽  
Kouji Yamamoto ◽  
Aya Kuchiba ◽  
Tatsuki Koyama
Keyword(s):  
Binary Classification ◽  
Classification Problem ◽  
Classification Problems ◽  
Summary Measure ◽  
Medical Field ◽  
Predictive Values ◽  
Binary Classification Problem ◽  
Multi Class Classification ◽  
Sensitivity Specificity ◽  
Measures Of Performance

AbstractA binary classification problem is common in medical field, and we often use sensitivity, specificity, accuracy, negative and positive predictive values as measures of performance of a binary predictor. In computer science, a classifier is usually evaluated with precision (positive predictive value) and recall (sensitivity). As a single summary measure of a classifier’s performance, F1 score, defined as the harmonic mean of precision and recall, is widely used in the context of information retrieval and information extraction evaluation since it possesses favorable characteristics, especially when the prevalence is low. Some statistical methods for inference have been developed for the F1 score in binary classification problems; however, they have not been extended to the problem of multi-class classification. There are three types of F1 scores, and statistical properties of these F1 scores have hardly ever been discussed. We propose methods based on the large sample multivariate central limit theorem for estimating F1 scores with confidence intervals.

Similarity Learning for Motion Estimation

2011 ◽  
pp. 130-151
Author(s):  
Shaohua Kevin Zhou ◽  
Jie Shao ◽  
Bogdan Georgescu ◽  
Dorin Comaniciu
Keyword(s):  
Motion Estimation ◽  
Binary Classification ◽  
Classification Problem ◽  
Similarity Function ◽  
Training Procedure ◽  
Image Pair ◽  
Similarity Learning ◽  
Model Complex ◽  
Binary Classification Problem ◽  
Test Errors

Motion estimation necessitates an appropriate choice of similarity function. Because generic similarity functions derived from simple assumptions are insufficient to model complex yet structured appearance variations in motion estimation, the authors propose to learn a discriminative similarity function to match images under varying appearances by casting image matching into a binary classification problem. They use the LogitBoost algorithm to learn the classifier based on an annotated database that exemplifies the structured appearance variations: An image pair in correspondence is positive and an image pair out of correspondence is negative. To leverage the additional distance structure of negatives, they present a location-sensitive cascade training procedure that bootstraps negatives for later stages of the cascade from the regions closer to the positives, which enables viewing a large number of negatives and steering the training process to yield lower training and test errors. The authors apply the learned similarity function to estimating the motion for the endocardial wall of left ventricle in echocardiography and to performing visual tracking. They obtain improved performances when comparing the learned similarity function with conventional ones.

scholarly journals A Machine Learning Approach to the Detection of Fetal Hypoxia during Labor and Delivery

AI Magazine ◽  
2012 ◽  
Vol 33 (2) ◽  
pp. 79 ◽  
Author(s):  
Philip A. Warrick ◽  
Emily F. Hamilton ◽  
Robert E. Kearney ◽  
Doina Precup
Keyword(s):  
Binary Classification ◽  
Classification Problem ◽  
Fetal Hypoxia ◽  
Novel Approach ◽  
Modern Health Care ◽  
Machine Learning Approach ◽  
Binary Classification Problem ◽  
Fetal Response ◽  
Monitoring Devices ◽  
Labor Monitoring

Labor monitoring is crucial in modern health care, as it can be used to detect (and help avoid) significant problems with the fetus. In this article we focus on detecting hypoxia (or oxygen deprivation), a very serious condition that can arise from different pathologies and can lead to life-long disability and death. We present a novel approach to hypoxia detection based on recordings of the uterine pressure and fetal heart rate, which are obtained using standard labor monitoring devices. The key idea is to learn models of the fetal response to signals from its environment. Then, we use the parameters of these models as attributes in a binary classification problem. A running count of pathological classifications over several time periods is taken to provide the current label for the fetus. We use a unique database of real clinical recordings, both from normal and pathological cases. Our approach classifies correctly more than half the pathological cases, 1.5 hours before delivery. These are cases that were missed by clinicians; early detection of this type would have allowed the physician to perform a Caesarean section, possibly avoiding the negative outcome.

Empirical Bayesian Binary Classification Forests Using Bootstrap Prior

2018 ◽  
Vol 7 (4.30) ◽  
pp. 170 ◽  
Author(s):  
Oyebayo Ridwan Olaniran ◽  
Mohd Asrul Affendi Bin Abdullah ◽  
Khuneswari A/P Gopal Pillay ◽  
Saidat Fehintola Olaniran
Keyword(s):  
Random Forest ◽  
Binary Classification ◽  
Classification Problem ◽  
Operating Characteristics ◽  
Empirical Bayesian ◽  
Receiver Operating Characteristics Curve ◽  
Binary Classification Problem ◽  
Input Variables ◽  
Sensitivity Specificity ◽  
Microarray Datasets

In this paper, we present a new method called Empirical Bayesian Random Forest (EBRF) for binary classification problem. The prior ingredient for the method was obtained using the bootstrap prior technique. EBRF addresses explicitly low accuracy problem in Random Forest (RF) classifier when the number of relevant input variables is relatively lower compared to the total number of input variables. The improvement was achieved by replacing the arbitrary subsample variable size with empirical Bayesian estimate.  An illustration of the proposed, and existing methods was performed using five high-dimensional microarray datasets that emanated from colon, breast, lymphoma and Central Nervous System (CNS) cancer tumours. Results from the data analysis revealed that EBRF provides reasonably higher accuracy, sensitivity, specificity and Area Under Receiver Operating Characteristics Curve (AUC) than RF in most of the datasets used.

MINIMAL CLASSIFICATION METHOD WITH ERROR-CORRECTING CODES FOR MULTICLASS RECOGNITION

2005 ◽  
Vol 19 (05) ◽  
pp. 663-680 ◽  
Author(s):  
DAYAN MANOHAR SIVALINGAM ◽  
NARENKUMAR PANDIAN ◽  
JEZEKIEL BEN-ARIE
Keyword(s):  
Binary Classification ◽  
Classification Problem ◽  
Error Correcting Codes ◽  
Classification Method ◽  
Support Vector ◽  
Object Image ◽  
Generalization Error ◽  
Image Library ◽  
Binary Classification Problem ◽  
Binary Classifiers

In this work, we develop an efficient technique to transform a multiclass recognition problem into a minimal binary classification problem using the Minimal Classification Method (MCM). The MCM requires only log 2 N classifications whereas the other methods require much more. For the classification, we use Support Vector Machine (SVM) based binary classifiers since they have superior generalization performance. Unlike the prevalent one-versus-one strategy (the bottom-up one-versus-one strategy is called tournament method) that separates only two classes at each classification, the binary classifiers in our method have to separate two groups of multiple classes. As a result, the probability of generalization error increases. This problem is alleviated by utilizing error correcting codes, which results only in a marginal increase in the required number of classifications. However, in comparison to the tournament method, our method requires only 50% of the classifications and still similar performance can be attained. The proposed solution is tested with the Columbia Object Image Library (COIL). We also test the performance under conditions of noise and occlusion.

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