Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models

Drift is an important issue that impairs the reliability of sensors, especially in gas sensors. The conventional method usually adopts the reference gas to compensate for the drift. However, its classification accuracy is not high. We propose a supervised learning algorithm that is based on multi-classifier integration for drift compensation in this paper, which incorporates drift compensation into the classification process, motivated by the fact that the goal of drift compensation is to improve the classification performance. In our method, with the obtained characteristics of sensors and the advantage of Support Vector Machine (SVM) in few-shot classification, the improved Long Shot Term Memory (LSTM) is integrated to build the multi-class classifier model. We tested the proposed approach on the publicly available time series dataset that was collected over three years by the metal-oxide gas sensors. The results clearly indicate the superiority of multiple classifier approach, which achieves higher classification accuracy as compared with different approaches during testing period with an ensemble of classifiers in the presence of sensor drift over time.

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Imbalanced Support Vector Machine Classification Based on Hyper-Sphere

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.339.384 ◽

2013 ◽

Vol 339 ◽

pp. 384-388

Author(s):

Cun He Li ◽

Rui Xue Chen ◽

Yi Zhao Ouyang

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Learning Algorithm ◽

Classification Performance ◽

Training Data ◽

Support Vector ◽

Classification Rate ◽

Important Method ◽

Selection Technique ◽

Unbalanced Classification

In classification, when the distribution of the training data between classes is uneven, the learning algorithm is generally dominated by the feature of the majority classes. Features in the minority classes are normally difficult to be fully recognized. Hyper-sphere support vector machine is an important method for unbalanced classification which is an important issue, but this algorithm has a defect. In order to significantly improve the classification performance of imbalanced datasets, we propose a new method based on Generalized Hyper-sphere Support Vector Machine to enhance the classification accuracy for the minority classes. Support vector machine (SVM) is then used as the base classifier to train the reprocessed dataset. Our experimental results demonstrate that the proposed selection technique improves the classification rate of the rare events, and it also improves the overall accuracy of SVM without data pre-processing.

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A new combined transient extraction method coupled with WO3 gas sensors for polluting gases classification

Sensor Review ◽

10.1108/sr-02-2021-0066 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Rabeb Faleh ◽

Sami Gomri ◽

Khalifa Aguir ◽

Abdennaceur Kachouri

Keyword(s):

Feature Extraction ◽

Gas Sensors ◽

Classification Accuracy ◽

Extraction Method ◽

Support Vector ◽

Classification Rate ◽

Content Type ◽

Feature Extraction Method ◽

Combined Features ◽

Discrimination Capacity

Purpose The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors. Design/methodology/approach To improve the classification accuracy and enhance selectivity, some combined features that were configured through the principal component analysis were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array. Findings The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application, as this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF. Originality/value Combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The ELM and SVM were used to identify the studied gases.

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Combustible Gas Classification Modeling using Support Vector Machine and Pairing Plot Scheme

Sensors ◽

10.3390/s19225018 ◽

2019 ◽

Vol 19 (22) ◽

pp. 5018 ◽

Cited By ~ 1

Author(s):

Kyu-Won Jang ◽

Jong-Hyeok Choi ◽

Ji-Hoon Jeon ◽

Hyun-Seok Kim

Keyword(s):

Support Vector Machine ◽

Gas Sensors ◽

Learning Algorithm ◽

Support Vector ◽

Detection Accuracy ◽

Minimum Concentration ◽

Industrial Sites ◽

Combustible Gas ◽

Proposed Model ◽

Combustible Gases

Combustible gases, such as CH4 and CO, directly or indirectly affect the human body. Thus, leakage detection of combustible gases is essential for various industrial sites and daily life. Many types of gas sensors are used to identify these combustible gases, but since gas sensors generally have low selectivity among gases, coupling issues often arise which adversely affect gas detection accuracy. To solve this problem, we built a decoupling algorithm with different gas sensors using a machine learning algorithm. Commercially available semiconductor sensors were employed to detect CH4 and CO, and then support vector machine (SVM) applied as a supervised learning algorithm for gas classification. We also introduced a pairing plot scheme to more effectively classify gas type. The proposed model classified CH4 and CO gases 100% correctly at all levels above the minimum concentration the gas sensors could detect. Consequently, SVM with pairing plot is a memory efficient and promising method for more accurate gas classification.

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Sentiment polarity classification of tweets using a extended dictionary

INTELIGENCIA ARTIFICIAL ◽

10.4114/intartif.vol21iss62pp1-12 ◽

2018 ◽

Vol 21 (62) ◽

pp. 1

Author(s):

Jorge E. Camargo ◽

Vladimir Vargas-Calderon ◽

Nelson Vargas ◽

Liliana Calderón-Benavides

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Classification Performance ◽

Semantic Relations ◽

Support Vector ◽

The Real ◽

Polarity Classification ◽

Real Academia ◽

Word Definitions

With the purpose of classifying text based on its sentiment polarity (positive or negative), we proposed an extension of a 68,000 tweets corpus through the inclusion of word definitions from a dictionary of the Real Academia Espa\~{n}ola de la Lengua (RAE). A set of 28,000 combinations of 6 Word2Vec and support vector machine parameters were considered in order to evaluate how positively would affect the inclusion of a RAE's dictionary definitions classification performance. We found that such a corpus extension significantly improve the classification accuracy. Therefore, we conclude that the inclusion of a RAE's dictionary increases the semantic relations learned by Word2Vec allowing a better classification accuracy.

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AN APPLICATION OF ROLL-INVARIANT POLARIMETRIC FEATURES FOR CROP CLASSIFICATION FROM MULTI-TEMPORAL RADARSAT-2 SAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-451-2018 ◽

2018 ◽

Vol XLII-1 ◽

pp. 451-456 ◽

Cited By ~ 1

Author(s):

M. Ustuner ◽

F. B. Sanli ◽

S. Abdikan ◽

M. T. Esetlili ◽

G. Bilgin

Keyword(s):

Classification Accuracy ◽

Alpha Angle ◽

Test Site ◽

Classification Performance ◽

Support Vector ◽

Observation Data ◽

Data Set ◽

Crop Classification ◽

Sar Data ◽

Spatio Temporal

Abstract. Crops are dynamically changing and time-critical in the growing season and therefore multitemporal earth observation data are needed for spatio-temporal monitoring of the crops. This study evaluates the impacts of classical roll-invariant polarimetric features such as entropy (H), anisotropy (A), mean alpha angle (&alpha;) and total scattering power (SPAN) for the crop classification from multitemporal polarimetric SAR data. For this purpose, five different data set were generated as following: (1) H&alpha;, (2) H&alpha;Span, (3) H&alpha;A, (4) H&alpha;ASpan and (5) coherency [T] matrix. A time-series of four PolSAR data (Radarsat-2) were acquired as 13 June, 01 July, 31 July and 24 August in 2016 for the test site located in Konya, Turkey. The test site is covered with crops (maize, potato, summer wheat, sunflower, and alfalfa). For the classification of the data set, three different models were used as following: Support Vector Machines (SVMs), Random Forests (RFs) and Naive Bayes (NB). The experimental results highlight that H&alpha;ASpan (91.43% for SVM, 92.25% for RF and 90.55% for NB) outperformed all other data sets in terms of classification performance, which explicitly proves the significant contribution of SPAN for the discrimination of crops. Highest classification accuracy was obtained as 92.25% by RF and H&alpha;ASpan while lowest classification accuracy was obtained as 66.99% by NB and H&alpha;. This experimental study suggests that roll-invariant polarimetric features can be considered as the powerful polarimetric components for the crop classification. In addition, the findings prove the added benefits of PolSAR data investigation by means of crop classification.

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Improving the classification performance with group lasso-based ranking method in high dimensional correlated data

Journal of Theoretical and Computational Chemistry ◽

10.1142/s021963362040009x ◽

2020 ◽

Vol 19 (03) ◽

pp. 2040009

Author(s):

Abhijeet R Patil ◽

Bong-Jin Choi ◽

Sangjin Kim

Keyword(s):

Classification Accuracy ◽

Geometric Mean ◽

Classification Performance ◽

Group Lasso ◽

Correlated Data ◽

Support Vector ◽

Cpg Sites ◽

Highly Correlated ◽

Selection Operator ◽

Sensitivity Specificity

The high-throughput correlated DNA methylation (DNAmeth) dataset generated from Illumina Infinium Human Methylation 27 (IIHM 27K) BeadChip assay. In the DNAmeth data, there are several CpG sites for every gene, and these grouped CpG sites are highly correlated. Most of the current filtering-based ranking (FBR) methods do not consider the group correlation structures. Obtaining the significant features with the FBR methods and applying these features to the classifiers to attain the best classification accuracy in highly correlated DNAmeth data is a challenging task. In this research, we introduce a resampling of group least absolute shrinkage and selection operator (glasso) FBR method capable of ignoring the unrelated features in the data considering the group correlation among the features. The various classifiers, such as random forests (RF), Naive Bayes (NB), and support vector machines (SVM) with the significant CpGs obtained from the proposed resampling of group lasso-based ranking (RGLR) method helped to boost the classification accuracy. Through simulated and experimental prostate DNAmeth data, we showed that higher performance of accuracy, sensitivity, specificity, and geometric mean is achieved by ignoring the unimportant CpG sites through the RGLR method.

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AGNES-SMOTE: An Oversampling Algorithm Based on Hierarchical Clustering and Improved SMOTE

Scientific Programming ◽

10.1155/2020/8837357 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Xin Wang ◽

Yue Yang ◽

Mingsong Chen ◽

Qin Wang ◽

Qin Qin ◽

...

Keyword(s):

Support Vector Machine ◽

Hierarchical Clustering ◽

Classification Accuracy ◽

Classification Performance ◽

Support Vector ◽

Seed Sample ◽

Imbalanced Datasets ◽

Centroid Method ◽

Svm Classification ◽

Sampling Process

Aiming at low classification accuracy of imbalanced datasets, an oversampling algorithm—AGNES-SMOTE (Agglomerative Nesting-Synthetic Minority Oversampling Technique) based on hierarchical clustering and improved SMOTE—is proposed. Its key procedures include hierarchically cluster majority samples and minority samples, respectively; divide minority subclusters on the basis of the obtained majority subclusters; select “seed sample” based on the sampling weight and probability distribution of minority subcluster; and restrict the generation of new samples in a certain area by centroid method in the sampling process. The combination of AGNES-SMOTE and SVM (Support Vector Machine) is presented to deal with imbalanced datasets classification. Experiments on UCI datasets are conducted to compare the performance of different algorithms mentioned in the literature. Experimental results indicate AGNES-SMOTE excels in synthesizing new samples and improves SVM classification performance on imbalanced datasets.

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SELF-ORGANIZING NEURAL GROVE: EFFICIENT NEURAL NETWORK ENSEMBLES USING PRUNED SELF-GENERATIONG NEURAL TREES

International Journal of Computing ◽

10.47839/ijc.12.3.601 ◽

2014 ◽

pp. 210-216

Author(s):

Hirotaka Inoue ◽

Kyoshiro Sugiyama

Keyword(s):

Neural Networks ◽

Classification Accuracy ◽

The Self ◽

Learning Ability ◽

Support Vector ◽

Multiple Classifier Systems ◽

Classifier Systems ◽

Computation Cost ◽

Multiple Classifier ◽

Self Organizing

R ecently, mul tiple classifier systems have been used for practical applications to improve classification accuracy. Self-generating neural networks are one of the most suitable base-classifiers for multiple classifier systems because of their simple settings and fast learning ability. However, the computation cost of the multiple classifier system based on self-generating neural networks increases in proportion to the numbers of self-gene rating neural networks. In this paper, w e propose a novel prunin g method for efficient classification and we call this model a self-organizing neural grove. Experiments have been conducted to compare the self-organizing neural grove with bagging and the self-organizing neural grove with boosting, and support vector machine. The results show that the self-organizing neural grove can improve its classification accuracy as well as reducing the computation cost.

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Classification of P2P Traffic Based on a Heteromorphic Ensemble Learning Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.2693 ◽

2014 ◽

Vol 687-691 ◽

pp. 2693-2697

Author(s):

Li Ding ◽

Li Mao ◽

Xiao Feng Wang

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Ensemble Learning ◽

Classification Accuracy ◽

Learning Algorithm ◽

Learning Model ◽

Support Vector ◽

Machine Learning Algorithm ◽

Data Environment

One single machine learning algorithm presents shortcomings when the data environment changes in the process of application. This article puts forward a heteromorphic ensemble learning model made up of bayes, support vector machine (SVM) and decision tree which classifies P2P traffic by voting principle. The experiment shows that the model can significantly improve the classification accuracy, and has a good stability.

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Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye

Sensors ◽

10.3390/s20092592 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2592

Author(s):

Xuemin Cheng ◽

Yong Ren ◽

Kaichang Cheng ◽

Jie Cao ◽

Qun Hao

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Image Data ◽

Recall Rate ◽

Classification Performance ◽

Support Vector ◽

Coordinate Systems ◽

Polar Coordinate

In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the method of recognition and classification for plankton images is discussed. The optimized classification and recognition networks are constructed. They are available for in situ plankton images, exploiting the advantages of both coordinate systems in the network training process. Fusing the two types of vectors and using them as the input for conventional machine learning models for classification, support vector machines (SVMs) are selected as the classifiers to combine these two features of vectors, coming from different image coordinate descriptions. The accuracy of the proposed model was markedly higher than those of the initial classical convolutional neural networks when using the in situ plankton image data, with the increases in classification accuracy and recall rate being 5.3% and 5.1% respectively. In addition, the proposed training method can improve the classification performance considerably when used on the public CIFAR-10 dataset.

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