scholarly journals Multi layered Stacked Ensemble Method with Feature Reduction Technique for Multi-Label Classification

2022 ◽  
Vol 2161 (1) ◽  
pp. 012074
Author(s):  
Hemavati ◽  
V Susheela Devi ◽  
R Aparna
Keyword(s):  
Ensemble Learning ◽  
Principal Component ◽  
Initial Step ◽  
Classification Problem ◽  
Feature Reduction ◽  
Training Dataset ◽  
Class Label ◽  
Label Information ◽  
Class Information

Abstract Nowadays, multi-label classification can be considered as one of the important challenges for classification problem. In this case instances are assigned more than one class label. Ensemble learning is a process of supervised learning where several classifiers are trained to get a better solution for a given problem. Feature reduction can be used to improve the classification accuracy by considering the class label information with principal Component Analysis (PCA). In this paper, stacked ensemble learning method with augmented class information PCA (CA PCA) is proposed for classification of multi-label data (SEMML). In the initial step, the dimensionality reduction step is applied, then the number of classifiers have to be chosen to apply on the original training dataset, then the stacking method is applied to it. By observing the results of experiments conducted are showing our proposed method is working better as compared to the existing methods.

Pallet-level Classification Using Principal Component Analysis in Ensemble Learning Model

Mekatronika ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 23-27
Author(s):  
Chun Sern Choong ◽  
Ahmad Fakhri Ab. Nasir ◽  
Muhammad Aizzat Zakaria ◽  
Anwar P.P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman
Keyword(s):  
Principal Component Analysis ◽  
Ensemble Learning ◽  
Radio Frequency Identification ◽  
Principal Component ◽  
Component Analysis ◽  
Forest Models ◽  
Random Forest Models ◽  
Frequency Identification ◽  
Selection Of

In this paper, we present a machine learning pipeline to solve a multiclass classification of radio frequency identification (RFID) signal strength. The goal is to identify ten pallet levels using nine statistical features derived from RFID signals and four various ensemble learning classification models. The efficacy of the models was evaluated by considering features that were dimensionally reduced via Principal Component Analysis (PCA) and original features. It was shown that the PCA reduced features could provide a better classification accuracy of the pallet levels in comparison to the selection of all features via Extra Tree and Random Forest models.

scholarly journals Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision

2021 ◽  
Vol 13 (5) ◽  
pp. 114
Author(s):  
Stefan Helmstetter ◽  
Heiko Paulheim
Keyword(s):  
Large Scale ◽  
Binary Classification ◽  
Classification Problem ◽  
Training Dataset ◽  
Fake News ◽  
Weak Supervision ◽  
Alternative Approach ◽  
Large Scale Dataset ◽  
Tweet Classification

The problem of automatic detection of fake news in social media, e.g., on Twitter, has recently drawn some attention. Although, from a technical perspective, it can be regarded as a straight-forward, binary classification problem, the major challenge is the collection of large enough training corpora, since manual annotation of tweets as fake or non-fake news is an expensive and tedious endeavor, and recent approaches utilizing distributional semantics require large training corpora. In this paper, we introduce an alternative approach for creating a large-scale dataset for tweet classification with minimal user intervention. The approach relies on weak supervision and automatically collects a large-scale, but very noisy, training dataset comprising hundreds of thousands of tweets. As a weak supervision signal, we label tweets by their source, i.e., trustworthy or untrustworthy source, and train a classifier on this dataset. We then use that classifier for a different classification target, i.e., the classification of fake and non-fake tweets. Although the labels are not accurate according to the new classification target (not all tweets by an untrustworthy source need to be fake news, and vice versa), we show that despite this unclean, inaccurate dataset, the results are comparable to those achieved using a manually labeled set of tweets. Moreover, we show that the combination of the large-scale noisy dataset with a human labeled one yields more advantageous results than either of the two alone.

scholarly journals Performance Comparison Of Ann Classifiers For Sleep Apnea Detection Based On Ecg Signal Analysis Using Hilbert Transform

2018 ◽  
Vol 17 (2) ◽  
pp. 7312-7325 ◽  
Author(s):  
Jyoti S Bali ◽  
Anilkumar V Nandi ◽  
P S Hiremath
Keyword(s):  
Sleep Apnea ◽  
Hilbert Transform ◽  
Signal Analysis ◽  
Principal Component ◽  
Feature Reduction ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Apnea Detection ◽  
Lm Algorithm

In this paper, a methodology for sleep apnea detection based on ECG signal analysis using Hilbert transform is proposed. The proposed work comprises a sequential procedure of preprocessing, QRS complex detection using Hilbert Transform, feature extraction from the detected QRS complex and the feature reduction using principal component analysis (PCA). Finally, the classification of the ECG signal recordings has been done using two different artificial neural networks (ANN), one trained with Levenberg-Marquardt (LM) algorithm and the other trained with Scaled Conjugate Gradient (SCG) method guided by K means clustering. The result of classification of the input ECG record is as either belonging to Apnea or Normal category. The performance measures of classification using the two classification algorithms are compared. The experimental results indicate that the SCG algorithm guided by K means clustering (ANN-SCG) has outperformed the LM algorithm (ANN-LM) by attaining accuracy, sensitivity and specificity values as 99.2%, 96% and 97% respectively, besides the saving achieved in terms of reduced number of principal components. Profiling time and mean square error of the ANN classifier trained with SCG algorithm is significantly reduced by 58% and 83%, respectively, as compared to LM algorithm.

Intelligent alarms using principal component analysis on hemodynamic monitoring (Preprint)

2019 ◽  
Author(s):  
Silje Skeide Fuglerud ◽  
Mikael Dyb Wedeld ◽  
Harald Martens ◽  
Nils Kristian Skjærvold
Keyword(s):  
Heart Rate ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Classification Problem ◽  
Component Analysis ◽  
Waveform Data ◽  
Alarm Fatigue ◽  
Physiological Variability ◽  
Monitoring Devices

BACKGROUND Patient monitors in modern hospitals give heartbeat waveform data that is reduced to aggregated variables and simple thresholds for alarms. Often, the monitors give a steady stream of non-specific alarms, leading to alarm fatigue in clinicians. An alarm can be seen as a classification problem, and by applying Principal Component Analysis (PCA) to the heart rate waveform of readily available monitoring devices, the accuracy of the classification of abnormality could be highly increased. OBJECTIVE To investigate whether physiological changes could be detected by looking at the heart rate waveform. METHODS A dataset of a healthy volunteer monitored with electrocardiography (ECG) and invasive blood pressure (BP) experiencing several tilts on a tilting table was investigated. A novel way of splitting continuous data based on the heartbeat was introduced. PCA was applied to classify the heartbeats. RESULTS A classification using only the aggregated variables heart rate (HR) and BP was able to correctly identify 20.7% of the heartbeats in the vertical tilt as abnormal. A classification using the full waveforms and combining the ECG and BP signals was able to correctly identify 83.5% of the heartbeats in the vertical tilt as abnormal. A humanistic machine learning (ML) method is then proposed based on the PCA classification. CONCLUSIONS A ML method for classification of physiological variability was described. The main novelty lies in the splitting of an ECG and BP signal by the heart rate and performing a PCA on the data-table.

A cross-entropy based stacking method in ensemble learning

2020 ◽  
Vol 39 (3) ◽  
pp. 4677-4688
Author(s):  
Weimin Ding ◽  
Shengli Wu
Keyword(s):  
Ensemble Learning ◽  
Gradient Descent ◽  
State Of The Art ◽  
Classification Problem ◽  
Cross Entropy ◽  
Stochastic Gradient Descent ◽  
Data Sets ◽  
Class Label ◽  
Meta Level ◽  
Learning Techniques

Stacking is one of the major types of ensemble learning techniques in which a set of base classifiers contributes their outputs to the meta-level classifier, and the meta-level classifier combines them so as to produce more accurate classifications. In this paper, we propose a new stacking algorithm that defines the cross-entropy as the loss function for the classification problem. The training process is conducted by using a neural network with the stochastic gradient descent technique. One major characteristic of our method is its treatment of each meta instance as a whole with one optimization model, which is different from some other stacking methods such as stacking with multi-response linear regression and stacking with multi-response model trees. In these methods each meta instance is divided into a set of sub-instances. Multiple models apply to those sub-instances and each for a class label. There is no connection between different models. It is very likely that our treatment is a better choice for finding suitable weights. Experiments with 22 data sets from the UCI machine learning repository show that the proposed stacking approach performs well. It outperforms all three base classifiers, several state-of-the-art stacking algorithms, and some other representative ensemble learning methods on average.

Hybridization of Rough Setsand Multi-ObjectiveEvolutionary Algorithms forClassificatory SignalDecomposition

2011 ◽  
pp. 204-227 ◽  
Author(s):  
Tomasz G. Smolinski ◽  
Astrid A. Prinz
Keyword(s):  
Rough Sets ◽  
Distance Measure ◽  
Principal Component ◽  
Classification Problem ◽  
Component Analysis ◽  
General Term ◽  
Signal Classification ◽  
To Come ◽  
Main Flaw

Classification of sampled continuous signals into one of a finite number of predefined classes is possible when some distance measure between the signals in the dataset is introduced. However, it is often difficult to come up with a “temporal” distance measure that is both accurate and efficient computationally. Thus in the problem of signal classification, extracting particular features that distinguish one process from another is crucial. Extraction of such features can take the form of a decomposition technique, such as Principal Component Analysis (PCA) or Independent Component Analysis (ICA). Both these algorithms have proven to be useful in signal classification. However, their main flaw lies in the fact that nowhere during the process of decomposition is the classificatory aptitude of the components taken into consideration. Thus the ability to differentiate between classes, based on the decomposition, is not assured. Classificatory decomposition (CD) is a general term that describes attempts to improve the effectiveness of signal decomposition techniques by providing them with “classification-awareness.” We propose a hybridization of multi-objective evolutionary algorithms (MOEA) and rough sets (RS) to perform the task of decomposition in the light of the underlying classification problem itself.

scholarly journals Classification of Red Blood Cells using Principal Component Analysis Technique

2019 ◽  
Vol 4 (2) ◽  
pp. 17-22 ◽  
Author(s):  
Jameela Ali Alkrimi ◽  
Sherna Aziz Tome ◽  
Loay E. George
Keyword(s):  
Principal Component Analysis ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Novel Approach ◽  
Analysis Technique

Principal component analysis (PCA) is based feature reduction that reduces the correlation of features. In this research, a novel approach is proposed by applying the PCA technique on various morphologies of red blood cells (RBCs). According to hematologists, this method successfully classified 40 different types of abnormal RBCs. The classification of RBCs into various distinct subtypes using three machine learning algorithms is important in clinical and laboratory tests for detecting blood diseases. The most common abnormal RBCs are considered as anemic. The RBC features are sufficient to identify the type of anemia and the disease that caused it. Therefore, we found that several features extracted from RBCs in the blood smear images are not significant for classification when observed independently but are significant when combined with other features. The number of feature vectors is reduced from 271 to 8 as time resuming in training and accuracy percentage increased to 98%.

scholarly journals Exploring the Extreme Learning Machine for Classification of Brain MRIs

2019 ◽  
Vol 9 (2) ◽  
pp. 3654-3657
Keyword(s):  
Extreme Learning Machine ◽  
Principal Component ◽  
Classification Performance ◽  
Feature Reduction ◽  
Brain Diseases ◽  
Discrete Wavelet ◽  
Learning Machine ◽  
Hidden Layer ◽  
Method Accuracy

Magnetic Resonance Imaging (MRI) technique of brain is the most important aspect of diagnosis of brain diseases. The manual analysis of MR images and identifying the brain diseases is tedious and error prone task for the radiologists and physicians. In this paper 2-Dimensional Discrete Wavelet Transformation (2D DWT) is used for feature extraction and Principal Component Analysis (PCA) is used for feature reduction. The three types of brain diseases i.e. Alzheimer, Glioma and Multiple Sclerosis are considered for this work. The Two Hidden layer Extreme learning Machine (TELM) is used for classification of samples into normal or pathological. The performance of the TELM is compared with basic ELM and the simulation results indicate that TELM outperformed the basic ELM method. Accuracy, Recall, Sensitivity and F-score are considered as the classification performance measures in this paper

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Chemometric Analysis for the Classification of some Groups of Drugs with Divergent Pharmacological Activity on the Basis of some Chromatographic and Molecular Modeling Parameters

2018 ◽  
Vol 21 (2) ◽  
pp. 125-137
Author(s):  
Jolanta Stasiak ◽  
Marcin Koba ◽  
Marcin Gackowski ◽  
Tomasz Baczek
Keyword(s):  
Correlation Analysis ◽  
Pharmacological Activity ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Cardiovascular Drugs ◽  
New Drugs ◽  
Analgesic Drugs ◽  
Starting Point ◽  
Chromatographic Parameters

Aim and Objective: In this study, chemometric methods as correlation analysis, cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) have been used to reduce the number of chromatographic parameters (logk/logkw) and various (e.g., 0D, 1D, 2D, 3D) structural descriptors for three different groups of drugs, such as 12 analgesic drugs, 11 cardiovascular drugs and 36 “other” compounds and especially to choose the most important data of them. Material and Methods: All chemometric analyses have been carried out, graphically presented and also discussed for each group of drugs. At first, compounds’ structural and chromatographic parameters were correlated. The best results of correlation analysis were as follows: correlation coefficients like R = 0.93, R = 0.88, R = 0.91 for cardiac medications, analgesic drugs, and 36 “other” compounds, respectively. Next, part of molecular and HPLC experimental data from each group of drugs were submitted to FA/PCA and CA techniques. Results: Almost all results obtained by FA or PCA, and total data variance, from all analyzed parameters (experimental and calculated) were explained by first two/three factors: 84.28%, 76.38 %, 69.71% for cardiovascular drugs, for analgesic drugs and for 36 “other” compounds, respectively. Compounds clustering by CA method had similar characteristic as those obtained by FA/PCA. In our paper, statistical classification of mentioned drugs performed has been widely characterized and discussed in case of their molecular structure and pharmacological activity. Conclusion: Proposed QSAR strategy of reduced number of parameters could be useful starting point for further statistical analysis as well as support for designing new drugs and predicting their possible activity.

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