Deep Nearest Class Mean Model for Incremental Odor Classification

Cheng, Yu; Wong, Kin-Yeung; Hung, Kevin; Li, Weitong; Li, Zhizhong; Zhang, Jun

doi:10.1109/tim.2018.2863438

Cited by 13 publications

(9 citation statements)

References 40 publications

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“…The best classification accuracies obtained with the MLSTM method for each of the three datasets presented were 99.99%, 95.17%, and 100%. Cheng et al [43] proposed a solution to the problem of dynamically growing odor datasets while both the training samples and number of classes increase over time. The solution uses a deep nearest class mean (DNCM) model based on a deep learning framework and the nearest class mean method.…”

Section: Related Workmentioning

confidence: 99%

Data Classification Methodology for Electronic Noses Using Uniform Manifold Approximation and Projection and Extreme Learning Machine

et al. 2021

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The classification and use of robust methodologies in sensor array applications of electronic noses (ENs) remain an open problem. Among the several steps used in the developed methodologies, data preprocessing improves the classification accuracy of this type of sensor. Data preprocessing methods, such as data transformation and data reduction, enable the treatment of data with anomalies, such as outliers and features, that do not provide quality information; in addition, they reduce the dimensionality of the data, thereby facilitating the tasks of a machine learning classifier. To help solve this problem, in this study, a machine learning methodology is introduced to improve signal processing and develop methodologies for classification when an EN is used. The proposed methodology involves a normalization stage to scale the data from the sensors, using both the well-known min−max approach and the more recent mean-centered unitary group scaling (MCUGS). Next, a manifold learning algorithm for data reduction is applied using uniform manifold approximation and projection (UMAP). The dimensionality of the data at the input of the classification machine is reduced, and an extreme learning machine (ELM) is used as a machine learning classifier algorithm. To validate the EN classification methodology, three datasets of ENs were used. The first dataset was composed of 3600 measurements of 6 volatile organic compounds performed by employing 16 metal-oxide gas sensors. The second dataset was composed of 235 measurements of 3 different qualities of wine, namely, high, average, and low, as evaluated by using an EN sensor array composed of 6 different sensors. The third dataset was composed of 309 measurements of 3 different gases obtained by using an EN sensor array of 2 sensors. A 5-fold cross-validation approach was used to evaluate the proposed methodology. A test set consisting of 25% of the data was used to validate the methodology with unseen data. The results showed a fully correct average classification accuracy of 1 when the MCUGS, UMAP, and ELM methods were used. Finally, the effect of changing the number of target dimensions on the reduction of the number of data was determined based on the highest average classification accuracy.

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Section: Related Workmentioning

confidence: 99%

Data Classification Methodology for Electronic Noses Using Uniform Manifold Approximation and Projection and Extreme Learning Machine

et al. 2021

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“…Six topologies of many-to-one, many-to-many plus global maximum pooling, many-to-many plus global average pooling, many-to-many plus many-to-one, bidirectional many-to-one, and many-to-many plus bidirectional many-to-one associated with the temporal layer are further explored, along with two cells of long short-term memory (LSTM) and gated recurrent unit (GRU). Instead of the usual feed-forward neural network with dropout and softmax making the final prediction, in the inference classifier, the predicting voting classifier (PVC) scheme based on the multiple nearest class mean (NCM) classifiers [8], SoftMax, and majority voting [9] are developed to determine the action class. In this study, two datasets from HMDB51 and UCF101 [2] are adopted to evaluate the proposed deep neural network (DNN).…”

Section: Introductionmentioning

confidence: 99%

“…To handle diversely growing data sets, it may be a good choice to use a model-free method. The classic model-free methods include the K-nearest neighbor (KNN) [21], and NCM classifiers [8]. The class-incremental learning mechanism was developed to train multiple NCM classifiers accompanied by feature representations simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Attention correlated appearance and motion feature followed temporal learning for activity recognition

Pham

Thanh

et al. 2023

IJECE

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<span lang="EN-US">Recent advances in deep neural networks have been successfully demonstrated with fairly good accuracy for multi-class activity identification. However, existing methods have limitations in achieving complex spatial-temporal dependencies. In this work, we design two stream fusion attention (2SFA) connected to a temporal bidirectional gated recurrent unit (GRU) one-layer model and classified by prediction voting classifier (PVC) to recognize the action in a video. Particularly in the proposed deep neural network (DNN), we present 2SFA for capturing appearance information from red green blue (RGB) and motion from optical flow, where both streams are correlated by proposed fusion attention (FA) as the input of a temporal network. On the other hand, the temporal network with a bi-directional temporal layer using a GRU single layer is preferred for temporal understanding because it yields practical merits against six topologies of temporal networks in the UCF101 dataset. Meanwhile, the new proposed classifier scheme called PVC employs multiple nearest class mean (NCM) and the SoftMax function to yield multiple features outputted from temporal networks, and then votes their properties for high-performance classifications. The experiments achieve the best average accuracy of 70.8% in HMDB51 and 91.9%, the second best in UCF101 in terms of 2DConvNet for action recognition.</span>

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“…The methods based on e-nose use electronic nose sensors to measure molecular odors, and to obtain high-dimensional odor data information as molecular feature information, the methods use machine learning or deep learning algorithms to perform odor prediction. Electronic nose technology is used in many aspects of life, such as food odor detection, industrial gas detection, disease detection, etc [6][7][8]. There are many studies on molecular odor impression prediction.…”

Section: Introductionmentioning

confidence: 99%

Odor-GCN: Graph Convolutional Network for Predicting Odor Impressions Based on Molecular Structures

Qiu

Cheng

Luo

et al. 2022

Preprint

Self Cite

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Predicting odor impression from molecular structure can help develop olfactory product such as perfumes, cosmetics and fragrances. At present, the methods of odor impression prediction can be divided into three categories: based on molecular physical and chemical characteristics, based on electronic nose, and based on mass spectrometry. In these methods, collecting odor data usually requires expensive equipment or time-consuming chemical and physical experiments. In order to overcome these difficulties, we proposed a molecular structure-based odor impression prediction method, which can be obtained in the existing chemical database (such as PubChem) without additional experiments. This greatly saves the time and cost of obtaining odor data. We believe that the olfactory information of odor is implicit in its molecular structure. First, molecules are represented as graphs, called molecule-graph, that is, atoms are regarded as the vertices of the graph, and covalent bonds are regarded as edges of the graph. Then a graph convolution network (GCN) was designed to extract olfactory information from molecule-graph. The GCN contains three con-volutional layers, an aggregation layer and a fully connected layer. In each graph convolution layer, vertices (atoms) can send message to the vertices (atoms) connected to it. The aggregation layer aggregates all the features of all vertices and calculates their average value. The fully connected layer outputs the odor impression value of the odor. Finally, we predicted the odor impressions (pleasantness , intensity and familiarity) for 476 odor single molecules. The best prediction of Pearson Correlation Coefficient for odor pleasantness impressions was r = 0.52 (p < 0.0000005), which is between the predicted and actual ratings, and the Mean Absolute Error was M AE = 8.53.

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Deep Nearest Class Mean Model for Incremental Odor Classification

Cited by 13 publications

References 40 publications

Data Classification Methodology for Electronic Noses Using Uniform Manifold Approximation and Projection and Extreme Learning Machine

Data Classification Methodology for Electronic Noses Using Uniform Manifold Approximation and Projection and Extreme Learning Machine

Attention correlated appearance and motion feature followed temporal learning for activity recognition

Odor-GCN: Graph Convolutional Network for Predicting Odor Impressions Based on Molecular Structures

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