scholarly journals Positive-Unlabeled Learning from Imbalanced Data

2021 ◽  
Author(s):  
Guangxin Su ◽  
Weitong Chen ◽  
Miao Xu
Keyword(s):  
State Of The Art ◽  
Binary Classification ◽  
Estimation Error ◽  
Imbalanced Data ◽  
Classification Problem ◽  
Learning Objective ◽  
Financial Fraud ◽  
Pu Learning ◽  
Objective State ◽  
Binary Classification Problem

Positive-unlabeled (PU) learning deals with the binary classification problem when only positive (P) and unlabeled (U) data are available, without negative (N) data. Existing PU methods perform well on the balanced dataset. However, in real applications such as financial fraud detection or medical diagnosis, data are always imbalanced. It remains unclear whether existing PU methods can perform well on imbalanced data. In this paper, we explore this problem and propose a general learning objective for PU learning targeting specially at imbalanced data. By this general learning objective, state-of-the-art PU methods based on optimizing a consistent risk can be adapted to conquer the imbalance. We theoretically show that in expectation, optimizing our learning objective is equivalent to learning a classifier on the oversampled balanced data with both P and N data available, and further provide an estimation error bound. Finally, experimental results validate the effectiveness of our proposal compared to state-of-the-art PU methods.

scholarly journals Averaging Forest for Online Vision

2009 ◽  
Vol 13 (4) ◽  
pp. 400-406 ◽  
Author(s):  
Hassab Elgawi Osman ◽  
Keyword(s):  
State Of The Art ◽  
Recognition Performance ◽  
Binary Classification ◽  
Empirical Studies ◽  
Classification Problem ◽  
Superior Performance ◽  
On Line ◽  
Binary Classification Problem ◽  
Training Examples ◽  
Object Features

In this study we consider vision as a binary classification problem, where an ensemble of decision-tree-based classifiers is trained on-line, new images are continuously added and the recognition decision is made without delay. Ensemble of decision trees is combined into aforestclassifier usingaveraging, generate an on-lineRandom Forest(RF) classifier. First we employ object descriptor model based on a bag of covariance matrices, to represent an object features, then run our on-line RF learner to select object descriptors and to learn object classifiers. Validation of our proposal with empirical studies in the GRAZ02 dataset domain demonstrates its superior performance over histogram-based counterparts, yielding object recognition performance comparable to state-of-the-art standard RF, AdaBoost, and SVM classifiers, even when only 10% of the training examples are used.

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.

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.

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.

Distance-based Global Descriptors for Multi-view Object Recognition

Robotica ◽  
2019 ◽  
Vol 38 (1) ◽  
pp. 106-117
Author(s):  
Prasanna Kannappan ◽  
Herbert G. Tanner
Keyword(s):  
Image Segmentation ◽  
Binary Classification ◽  
Classification Problem ◽  
Target Object ◽  
Global Feature ◽  
Data Set ◽  
Multiple Images ◽  
Feature Descriptors ◽  
Single Target ◽  
Binary Classification Problem

SummaryThe paper reports on a new multi-view algorithm that combines information from multiple images of a single target object, captured at different distances, to determine the identity of an object. Due to the use of global feature descriptors, the method does not involve image segmentation. The performance of the algorithm has been evaluated on a binary classification problem for a data set consisting of a series of underwater images.

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