General solution and learning method for binary classification with performance constraints

2008 ◽  
Vol 29 (10) ◽  
pp. 1455-1465 ◽  
Author(s):  
Abdenour Bounsiar ◽  
Pierre Beauseroy ◽  
Edith Grall-Maës
Keyword(s):  
General Solution ◽  
Binary Classification ◽  
Learning Method ◽  
Performance Constraints

scholarly journals Cold-start playlist recommendation with multitask learning

2018 ◽  
Author(s):  
Dawei Chen ◽  
Cheng Soon Ong ◽  
Aditya Krishna Menon
Keyword(s):  
Binary Classification ◽  
Optimal Solution ◽  
Cold Start ◽  
Multitask Learning ◽  
Learning Objective ◽  
Learning Method ◽  
Empirical Results ◽  
Bipartite Ranking ◽  
Ranking Loss

Playlist recommendation involves producing a set of songs that a user might enjoy. We investigate this problem in three cold-start scenarios: (i) cold playlists, where we recommend songs to form new personalised playlists for an existing user; (ii) cold users, where we recommend songs to form new playlists for a new user; and (iii) cold songs, where we recommend newly released songs to extend users’ existing playlists. We propose a flexible multitask learning method to deal with all three settings. The method learns from user-curated playlists, and encourages songs in a playlist to be ranked higher than those that are not by minimising a bipartite ranking loss. Inspired by an equivalence between bipartite ranking and binary classification, we show how one can efficiently approximate an optimal solution of the multitask learning objective by minimising a classification loss. Empirical results on two real playlist datasets show the proposed approach has good performance for cold-start playlist recommendation.

scholarly journals Cold-start playlist recommendation with multitask learning

2018 ◽  
Author(s):  
Dawei Chen ◽  
Cheng Soon Ong ◽  
Aditya Krishna Menon
Keyword(s):  
Binary Classification ◽  
Optimal Solution ◽  
Cold Start ◽  
Multitask Learning ◽  
Learning Objective ◽  
Learning Method ◽  
Empirical Results ◽  
Bipartite Ranking ◽  
Ranking Loss

Playlist recommendation involves producing a set of songs that a user might enjoy. We investigate this problem in three cold-start scenarios: (i) cold playlists, where we recommend songs to form new personalised playlists for an existing user; (ii) cold users, where we recommend songs to form new playlists for a new user; and (iii) cold songs, where we recommend newly released songs to extend users’ existing playlists. We propose a flexible multitask learning method to deal with all three settings. The method learns from user-curated playlists, and encourages songs in a playlist to be ranked higher than those that are not by minimising a bipartite ranking loss. Inspired by an equivalence between bipartite ranking and binary classification, we show how one can efficiently approximate an optimal solution of the multitask learning objective by minimising a classification loss. Empirical results on two real playlist datasets show the proposed approach has good performance for cold-start playlist recommendation.

A Robust and Versatile Multi-View Learning Framework for the Detection of Deviant Business Process Instances

2016 ◽  
Vol 25 (04) ◽  
pp. 1740003 ◽  
Author(s):  
Alfredo Cuzzocrea ◽  
Francesco Folino ◽  
Massimo Guarascio ◽  
Luigi Pontieri
Keyword(s):  
Business Process ◽  
Binary Classification ◽  
Real Life ◽  
Classification Model ◽  
Learning Method ◽  
Detection Model ◽  
Learning Framework ◽  
Deviance Detection ◽  
Feature Based ◽  
Comprehensive Framework

Increasing attention has been paid to the detection and analysis of “deviant” instances of a business process that are connected with some kind of “hidden” undesired behavior (e.g. frauds and faults). In particular, several recent works faced the problem of inducing a binary classification model (here named deviance detection model ) that can discriminate between deviant traces and normal ones, based on a set of historical log traces (labeled as either deviant or normal). Current solutions rely on applying standard classifier-induction methods to a feature-based representation of the given traces, where the features include sequence-based patterns extracted from the corresponding sequences of activities. However, there is no consensus on which kinds of patterns are the most suitable for such a task. On the other hand, mixing multiple pattern families together may produce a heterogenous, redundant and sparse representation of the traces that likely leads to poor deviance detection models. In this paper, we propose an ensemble-learning method for solving this problem, where multiple base classifiers are trained on different feature-based views of the log (each obtained by mapping the traces onto a distinguished collection of patterns). A stacking procedure is used to combine the discovered base models into an overall probabilistic model that associates any new trace with an estimate of the probability that it reflects a deviant process instance. This helps the analyst prioritize the inspection of the cases that are more likely to be deviant. The method also takes advantage of all nonstructural data available in the log, and employs a resampling mechanism to deal with the rarity of deviances in the training log. It has been conceived as the core of a comprehensive framework for detecting and analyzing business process deviances. The framework supports the analyst to investigate suspect deviances, and provides some feedback to the learning method for improving the accuracy of the discovered deviance detection models. Tests on several real-life datasets proved the validity of the approach, as concerns its capability to discover an accurate deviance detection model, and to effectively exploit new (originally unlabeled) traces via active learning and self-training mechanisms.

scholarly journals A Stacking-based Ensemble Learning Method for Outlier Detection

2021 ◽  
Author(s):  
Abdul Ahad Abro
Keyword(s):  
Machine Learning ◽  
Outlier Detection ◽  
Area Under Curve ◽  
Binary Classification ◽  
Ensemble Classifier ◽  
Performance Criteria ◽  
Learning Problems ◽  
Learning Method ◽  
Rotation Forest ◽  
Research Areas

Abstract Outlier detection is considered as one of the crucial research areas for data mining. Many methods have been studied widely and utilized for achieving better results in outlier detection from existing literature; however, the effects of these few ways are inadequate. In this paper, a stacking-based ensemble classifier has been proposed along with four base learners (namely, Rotation Forest, Random Forest, Bagging and Boosting) and a Meta-learner (namely, Logistic Regression) to progress the outlier detection performance. The proposedmechanism is evaluated on five datasets from the ODDS library by adopting five performance criteria. The experimental outcomes demonstrate that the proposed method outperforms than the conventional ensemble approaches concerning the accuracy, AUC (Area Under Curve), precision, recall and Fmeasure values.This method can be used for image recognition and machine learning problems, such as binary classification.

scholarly journals Emotion Recognition of EEG Signals Based on the Ensemble Learning Method: AdaBoost

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yu Chen ◽  
Rui Chang ◽  
Jifeng Guo
Keyword(s):  
Emotion Recognition ◽  
Ensemble Learning ◽  
Binary Classification ◽  
Feature Selection Method ◽  
Learning Method ◽  
Eeg Signals ◽  
Data Set ◽  
Linear Discriminant ◽  
Time Frequency ◽  
Nonlinear Features

In recent years, with the continuous development of artificial intelligence and brain-computer interface technology, emotion recognition based on physiological signals, especially, electroencephalogram (EEG) signals, has become a popular research topic and attracted wide attention. However, how to extract effective features from EEG signals and accurately recognize them by classifiers have also become an increasingly important task. Therefore, in this paper, we propose an emotion recognition method of EEG signals based on the ensemble learning method, AdaBoost. First, we consider the time domain, time-frequency domain, and nonlinear features related to emotion, extract them from the preprocessed EEG signals, and fuse the features into an eigenvector matrix. Then, the linear discriminant analysis feature selection method is used to reduce the dimensionality of the features. Next, we use the optimized feature sets and train a classifier based on the ensemble learning method, AdaBoost, for binary classification. Finally, the proposed method has been tested in the DEAP data set on four emotional dimensions: valence, arousal, dominance, and liking. The proposed method is proved to be effective in emotion recognition, and the best average accuracy rate can reach up to 88.70% on the dominance dimension. Compared with other existing methods, the performance of the proposed method is significantly improved.

scholarly journals Research of Metric Learning-Based Method for Person Re-Identification by Intelligent Computer Vision Technology

2021 ◽  
Vol 2083 (4) ◽  
pp. 042013
Author(s):  
Qiyu Rao
Keyword(s):  
Binary Classification ◽  
Metric Learning ◽  
Training Data ◽  
Identification Accuracy ◽  
Learning Method ◽  
Learning Methods ◽  
The Difference ◽  
Multi Class Classification ◽  
Fitting Problem ◽  
Metric Model

Abstract Person re-identification technology aims to establish an efficient metric model for similarity distance measurement of pedestrian images. Candidate images captured by different camera views are ranked according to their similarities to the target individual. However, the metric learning-based method, which is commonly used in similarity measurement, often failed in person re-identification tasks due to the drastic variations in appearance. The main reason for its low identification accuracy is that the metric learning method is over-fitting to the training data. Several types of metric learning methods which differ from each other by the distribution of sample pairs were summarized in this article for analysing and easing the metric learning methods’ over-fitting problem. Three different metric learning methods were tested on the VIPeR dataset. The distributions of the distance of the positive/negative training/test pairs are displayed to demonstrate the over-fitting problem. Then, a new metric model was proposed by combining the thoughts of binary classification and multi-class classification. Related verification experiments were conducted on VIPeR dataset. Besides, the semi-supervised metric learning approach was introduced to alleviate the over-fitting problem. The experimental results reflect gap between training pairs and test pairs in the metric subspace. Therefore, reducing the difference between training data and test data is a promising way to improve the identification accuracy of metric learning method.

scholarly journals Cold-start playlist recommendation with multitask learning

2018 ◽  
Author(s):  
Dawei Chen ◽  
Cheng Soon Ong ◽  
Aditya Krishna Menon
Keyword(s):  
Binary Classification ◽  
Optimal Solution ◽  
Cold Start ◽  
Multitask Learning ◽  
Learning Objective ◽  
Learning Method ◽  
Empirical Results ◽  
Bipartite Ranking ◽  
Ranking Loss

Playlist recommendation involves producing a set of songs that a user might enjoy. We investigate this problem in three cold-start scenarios: (i) cold playlists, where we recommend songs to form new personalised playlists for an existing user; (ii) cold users, where we recommend songs to form new playlists for a new user; and (iii) cold songs, where we recommend newly released songs to extend users’ existing playlists. We propose a flexible multitask learning method to deal with all three settings. The method learns from user-curated playlists, and encourages songs in a playlist to be ranked higher than those that are not by minimising a bipartite ranking loss. Inspired by an equivalence between bipartite ranking and binary classification, we show how one can efficiently approximate an optimal solution of the multitask learning objective by minimising a classification loss. Empirical results on two real playlist datasets show the proposed approach has good performance for cold-start playlist recommendation.

The Role of VLBI in the Establishment of Coordinate Systems

1975 ◽  
Vol 26 ◽  
pp. 293-295 ◽  
Author(s):  
I. Zhongolovitch
Keyword(s):  
General Solution ◽  
Future Development ◽  
Rational Approach ◽  
Radio Telescopes ◽  
Coordinate Systems ◽  
Tectonic Plates ◽  
Astronomical Observations ◽  
Optical Instruments ◽  
Fundamental Polyhedron

Considering the future development and general solution of the problem under consideration and also the high precision attainable by astronomical observations, the following procedure may be the most rational approach:1. On the main tectonic plates of the Earth’s crust, powerful movable radio telescopes should be mounted at the same points where standard optical instruments are installed. There should be two stations separated by a distance of about 6 to 8000 kilometers on each plate. Thus, we obtain a fundamental polyhedron embracing the whole Earth with about 10 to 12 apexes, and with its sides represented by VLBI.

Bases for Strategy Choice and Strategy Transitions in Binary Classification Tasks

2004 ◽  
Author(s):  
Lyle E. Bourne ◽  
Alice F. Healy ◽  
James A. Kole ◽  
William D. Raymond
Keyword(s):  
Binary Classification ◽  
Strategy Choice ◽  
Classification Tasks
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