Attribute Noise Robust Binary Classification (Student Abstract)

In outdoor Light Detection and Ranging (lidar)point cloud classification, finding the discriminative features for point cloud perception and scene understanding represents one of the great challenges. The features derived from defect-laden (i.e., noise, outliers, occlusions and irregularities) and raw outdoor LiDAR scans usually contain redundant and irrelevant information which adversely affects the accuracy of point semantic labeling. Moreover, point cloud features of different views have a capability to express different attributes of the same point. The simplest way of concatenating these features of different views cannot guarantee the applicability and effectiveness of the fused features. To solve these problems and achieve outdoor point cloud classification with fewer training samples, we propose a novel multi-view features and classifiers’ joint learning framework. The proposed framework uses label consistency and local distribution consistency of multi-space constraints for multi-view point cloud features extraction and classification. In the framework, the manifold learning is used to carry out subspace joint learning of multi-view features by introducing three kinds of constraints, i.e., local distribution consistency of feature space and position space, label consistency among multi-view predicted labels and ground truth, and label consistency among multi-view predicted labels. The proposed model can be well trained by fewer training points, and an iterative algorithm is used to solve the joint optimization of multi-view feature projection matrices and linear classifiers. Subsequently, the multi-view features are fused and used for point cloud classification effectively. We evaluate the proposed method on five different point cloud scenes and experimental results demonstrate that the classification performance of the proposed method is at par or outperforms the compared algorithms.

Download Full-text

THE USE OF MACHINE LEARNING METHODS FOR BINARY CLASSIFICATION OF THE WORKING CONDITION OF BEARINGS USING THE SIGNALS OF VIBRATION ACCELERATION

Bulletin of National Technical University KhPI Series System Analysis Control and Information Technologies ◽

10.20998/2079-0023.2021.02.03 ◽

2021 ◽

pp. 15-22

Author(s):

Ruslan Babudzhan ◽

Konstantyn Isaienkov ◽

Danilo Krasiy ◽

Oleksii Vodka ◽

Ivan Zadorozhny ◽

...

Keyword(s):

Machine Learning ◽

Binary Classification ◽

Fractal Dimensions ◽

Feature Space ◽

Training Data ◽

Supervised Machine Learning ◽

Support Vector ◽

Data Sets ◽

Vibration Acceleration ◽

K Nearest Neighbors

The paper investigates the relationship between vibration acceleration of bearings with their operational state. To determine these dependencies, a testbench was built and 112 experiments were carried out with different bearings: 100 bearings that developed an internal defect during operation and 12bearings without a defect. From the obtained records, a dataset was formed, which was used to build classifiers. Dataset is freely available. A methodfor classifying new and used bearings was proposed, which consists in searching for dependencies and regularities of the signal using descriptive functions: statistical, entropy, fractal dimensions and others. In addition to processing the signal itself, the frequency domain of the bearing operationsignal was also used to complement the feature space. The paper considered the possibility of generalizing the classification for its application on thosesignals that were not obtained in the course of laboratory experiments. An extraneous dataset was found in the public domain. This dataset was used todetermine how accurate a classifier was when it was trained and tested on significantly different signals. Training and validation were carried out usingthe bootstrapping method to eradicate the effect of randomness, given the small amount of training data available. To estimate the quality of theclassifiers, the F1-measure was used as the main metric due to the imbalance of the data sets. The following supervised machine learning methodswere chosen as classifier models: logistic regression, support vector machine, random forest, and K nearest neighbors. The results are presented in theform of plots of density distribution and diagrams.

Download Full-text

Cold-start playlist recommendation with multitask learning

10.7287/peerj.preprints.27383v2 ◽

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.

Download Full-text

Reduced Dilation-Erosion Perceptron for Binary Classification

Mathematics ◽

10.3390/math8040512 ◽

2020 ◽

Vol 8 (4) ◽

pp. 512

Author(s):

Marcos Eduardo Valle

Keyword(s):

Mathematical Morphology ◽

Computing Methodology ◽

Convex Combination ◽

Binary Classification ◽

Feature Space ◽

Support Vector ◽

Training Set ◽

Linear Lattice ◽

Hinge Loss ◽

Classification Tasks

Dilation and erosion are two elementary operations from mathematical morphology, a non-linear lattice computing methodology widely used for image processing and analysis. The dilation-erosion perceptron (DEP) is a morphological neural network obtained by a convex combination of a dilation and an erosion followed by the application of a hard-limiter function for binary classification tasks. A DEP classifier can be trained using a convex-concave procedure along with the minimization of the hinge loss function. As a lattice computing model, the DEP classifier assumes the feature and class spaces are partially ordered sets. In many practical situations, however, there is no natural ordering for the feature patterns. Using concepts from multi-valued mathematical morphology, this paper introduces the reduced dilation-erosion (r-DEP) classifier. An r-DEP classifier is obtained by endowing the feature space with an appropriate reduced ordering. Such reduced ordering can be determined using two approaches: one based on an ensemble of support vector classifiers (SVCs) with different kernels and the other based on a bagging of similar SVCs trained using different samples of the training set. Using several binary classification datasets from the OpenML repository, the ensemble and bagging r-DEP classifiers yielded mean higher balanced accuracy scores than the linear, polynomial, and radial basis function (RBF) SVCs as well as their ensemble and a bagging of RBF SVCs.

Download Full-text

Cold-start playlist recommendation with multitask learning

10.7287/peerj.preprints.27383 ◽

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.

Download Full-text

Synthetic Data Augmentation and Deep Learning for the Fault Diagnosis of Rotating Machines

Mathematics ◽

10.3390/math9182336 ◽

2021 ◽

Vol 9 (18) ◽

pp. 2336

Author(s):

Asif Khan ◽

Hyunho Hwang ◽

Heung Soo Kim

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Data Augmentation ◽

Binary Classification ◽

Synthetic Data ◽

Feature Space ◽

Test Accuracy ◽

Rotating Machines ◽

Health States ◽

Virtual Sensors

As failures in rotating machines can have serious implications, the timely detection and diagnosis of faults in these machines is imperative for their smooth and safe operation. Although deep learning offers the advantage of autonomously learning the fault characteristics from the data, the data scarcity from different health states often limits its applicability to only binary classification (healthy or faulty). This work proposes synthetic data augmentation through virtual sensors for the deep learning-based fault diagnosis of a rotating machine with 42 different classes. The original and augmented data were processed in a transfer learning framework and through a deep learning model from scratch. The two-dimensional visualization of the feature space from the original and augmented data showed that the latter’s data clusters are more distinct than the former’s. The proposed data augmentation showed a 6–15% improvement in training accuracy, a 44–49% improvement in validation accuracy, an 86–98% decline in training loss, and a 91–98% decline in validation loss. The improved generalization through data augmentation was verified by a 39–58% improvement in the test accuracy.

Download Full-text

Model-free classification of multivariate time-series based on epsilon-complexity theory.

Transaction Kola Science Cetnre ◽

10.37614/2307-5252.2020.8.11.023 ◽

2020 ◽

Vol 11 (8-2020) ◽

pp. 176-178

Author(s):

B.S. Darkhovsky ◽

◽

Y.A. Dubnov ◽

A.Y. Popkov ◽

◽

...

Keyword(s):

Time Series ◽

Binary Classification ◽

Multivariate Time Series ◽

Feature Space ◽

Classification Problem ◽

Eeg Signals ◽

Real Numbers ◽

Model Free ◽

Free Classification

This work is devoted to a new model-free approach to a problem of binary classification of multivariate time-series. The approach is based on the original theory of epsilon-complexity which allows almost every mapping that satisfies Hoelder condition, be characterized by a pair of real numbers –complexity coefficients. Thus we can form a feature space in which a classification problem can be formulated and solved. We provide an example of classification of real EEG signals.

Download Full-text