Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification

In developing a few-shot classification model using deep networks, the limited number of samples in each class causes difficulty in utilizing statistical characteristics of the class distributions. In this paper, we propose a method to treat this difficulty by combining a probabilistic similarity based on intra-class statistics with a metric-based few-shot classification model. Noting that the probabilistic similarity estimated from intra-class statistics and the classifier of conventional few-shot classification models have a common assumption on the class distributions, we propose to apply the probabilistic similarity to obtain loss value for episodic learning of embedding network as well as to classify unseen test data. By defining the probabilistic similarity as the probability density of difference vectors between two samples with the same class label, it is possible to obtain a more reliable estimate of the similarity especially for the case of large number of classes. Through experiments on various benchmark data, we confirm that the probabilistic similarity can improve the classification performance, especially when the number of classes is large.

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Active Learning with Query Generation for Cost-Effective Text Classification

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.6133 ◽

2020 ◽

Vol 34 (04) ◽

pp. 6583-6590

Author(s):

Yi-Fan Yan ◽

Sheng-Jun Huang ◽

Shaoyi Chen ◽

Meng Liao ◽

Jin Xu

Keyword(s):

Active Learning ◽

Text Classification ◽

Large Scale ◽

Cost Effective ◽

Classification Performance ◽

Unlabeled Data ◽

Classification Model ◽

Class Label ◽

Text Document ◽

Query Generation

Labeling a text document is usually time consuming because it requires the annotator to read the whole document and check its relevance with each possible class label. It thus becomes rather expensive to train an effective model for text classification when it involves a large dataset of long documents. In this paper, we propose an active learning approach for text classification with lower annotation cost. Instead of scanning all the examples in the unlabeled data pool to select the best one for query, the proposed method automatically generates the most informative examples based on the classification model, and thus can be applied to tasks with large scale or even infinite unlabeled data. Furthermore, we propose to approximate the generated example with a few summary words by sparse reconstruction, which allows the annotators to easily assign the class label by reading a few words rather than the long document. Experiments on different datasets demonstrate that the proposed approach can effectively improve the classification performance while significantly reduce the annotation cost.

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Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽

10.3390/en14071809 ◽

2021 ◽

Vol 14 (7) ◽

pp. 1809

Author(s):

Mohammed El Amine Senoussaoui ◽

Mostefa Brahami ◽

Issouf Fofana

Keyword(s):

Machine Learning ◽

Random Forest ◽

Oil Quality ◽

Principal Component ◽

Condition Assessment ◽

Classification Performance ◽

Transformer Oil ◽

Classification Model ◽

Insulation Degradation ◽

Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

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BiLabel-Specific Features for Multi-Label Classification

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3458283 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1-23

Author(s):

Min-Ling Zhang ◽

Jun-Peng Fang ◽

Yi-Bo Wang

Keyword(s):

Predictive Models ◽

Comparative Studies ◽

State Of The Art ◽

Classification Model ◽

Generation Process ◽

Prototype Selection ◽

Class Label ◽

Benchmark Datasets ◽

Label Correlations ◽

Class Labels

In multi-label classification, the task is to induce predictive models which can assign a set of relevant labels for the unseen instance. The strategy of label-specific features has been widely employed in learning from multi-label examples, where the classification model for predicting the relevancy of each class label is induced based on its tailored features rather than the original features. Existing approaches work by generating a group of tailored features for each class label independently, where label correlations are not fully considered in the label-specific features generation process. In this article, we extend existing strategy by proposing a simple yet effective approach based on BiLabel-specific features. Specifically, a group of tailored features is generated for a pair of class labels with heuristic prototype selection and embedding. Thereafter, predictions of classifiers induced by BiLabel-specific features are ensembled to determine the relevancy of each class label for unseen instance. To thoroughly evaluate the BiLabel-specific features strategy, extensive experiments are conducted over a total of 35 benchmark datasets. Comparative studies against state-of-the-art label-specific features techniques clearly validate the superiority of utilizing BiLabel-specific features to yield stronger generalization performance for multi-label classification.

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Attention-based deep learning networks for identification of human gait using radar micro-Doppler spectrograms

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078721000830 ◽

2021 ◽

pp. 1-6

Author(s):

Hannah Garcia Doherty ◽

Roberto Arnaiz Burgueño ◽

Roeland P. Trommel ◽

Vasileios Papanastasiou ◽

Ronny I. A. Harmanny

Keyword(s):

Neural Networks ◽

Feature Vector ◽

Classification Performance ◽

Input Image ◽

Human Gait ◽

Learning Networks ◽

Class Label ◽

Deep Convolutional Neural Networks ◽

Network Layers ◽

Feature Dimension

Abstract Identification of human individuals within a group of 39 persons using micro-Doppler (μ-D) features has been investigated. Deep convolutional neural networks with two different training procedures have been used to perform classification. Visualization of the inner network layers revealed the sections of the input image most relevant when determining the class label of the target. A convolutional block attention module is added to provide a weighted feature vector in the channel and feature dimension, highlighting the relevant μ-D feature-filled areas in the image and improving classification performance.

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Semi-Supervised Classification and its Application to Filtering IDS False Positives

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.2309 ◽

2013 ◽

Vol 427-429 ◽

pp. 2309-2312

Author(s):

Hai Bin Mei ◽

Ming Hua Zhang

Keyword(s):

Supervised Learning ◽

Supervised Classification ◽

Classification Performance ◽

False Positives ◽

Training Data ◽

Classification Model ◽

Classification Technique

Alert classifiers built with the supervised classification technique require large amounts of labeled training alerts. Preparing for such training data is very difficult and expensive. Thus accuracy and feasibility of current classifiers are greatly restricted. This paper employs semi-supervised learning to build alert classification model to reduce the number of needed labeled training alerts. Alert context properties are also introduced to improve the classification performance. Experiments have demonstrated the accuracy and feasibility of our approach.

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Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2019.1.3420 ◽

2019 ◽

Vol 14 (1) ◽

pp. 124-134 ◽

Cited By ~ 2

Author(s):

Shuai Zhang ◽

Yong Chen ◽

Xiaoling Huang ◽

Yishuai Cai

Keyword(s):

Neural Network ◽

Differential Evolution ◽

Convolutional Neural Network ◽

Text Classification ◽

Differential Evolution Algorithm ◽

Classification Performance ◽

Classification Model ◽

Evolution Algorithm ◽

Classification Prediction

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.

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Liver Cancer Classification Model Using Hybrid Feature Selection Based on Class-Dependent Technique for the Central Region of Thailand

Information ◽

10.3390/info10060187 ◽

2019 ◽

Vol 10 (6) ◽

pp. 187

Author(s):

Rattanawadee Panthong ◽

Anongnart Srivihok

Keyword(s):

Feature Selection ◽

Liver Cancer ◽

Predictive Model ◽

Information Gain ◽

Classification Performance ◽

Cancer Classification ◽

Feature Subset Selection ◽

Classification Model ◽

Feature Subset ◽

Cancer Data

Liver cancer data always consist of a large number of multidimensional datasets. A dataset that has huge features and multiple classes may be irrelevant to the pattern classification in machine learning. Hence, feature selection improves the performance of the classification model to achieve maximum classification accuracy. The aims of the present study were to find the best feature subset and to evaluate the classification performance of the predictive model. This paper proposed a hybrid feature selection approach by combining information gain and sequential forward selection based on the class-dependent technique (IGSFS-CD) for the liver cancer classification model. Two different classifiers (decision tree and naïve Bayes) were used to evaluate feature subsets. The liver cancer datasets were obtained from the Cancer Hospital Thailand database. Three ensemble methods (ensemble classifiers, bagging, and AdaBoost) were applied to improve the performance of classification. The IGSFS-CD method provided good accuracy of 78.36% (sensitivity 0.7841 and specificity 0.9159) on LC_dataset-1. In addition, LC_dataset II delivered the best performance with an accuracy of 84.82% (sensitivity 0.8481 and specificity 0.9437). The IGSFS-CD method achieved better classification performance compared to the class-independent method. Furthermore, the best feature subset selection could help reduce the complexity of the predictive model.

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Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media

Applied Sciences ◽

10.3390/app10124180 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4180 ◽

Cited By ~ 2

Author(s):

Komal Florio ◽

Valerio Basile ◽

Marco Polignano ◽

Pierpaolo Basile ◽

Viviana Patti

Keyword(s):

Social Media ◽

Hate Speech ◽

Time Window ◽

Classification Performance ◽

Fine Tuning ◽

Classification Model ◽

Temporal Distance ◽

Speech Detection ◽

Highly Sensitive

The availability of large annotated corpora from social media and the development of powerful classification approaches have contributed in an unprecedented way to tackle the challenge of monitoring users’ opinions and sentiments in online social platforms across time. Such linguistic data are strongly affected by events and topic discourse, and this aspect is crucial when detecting phenomena such as hate speech, especially from a diachronic perspective. We address this challenge by focusing on a real case study: the “Contro l’odio” platform for monitoring hate speech against immigrants in the Italian Twittersphere. We explored the temporal robustness of a BERT model for Italian (AlBERTo), the current benchmark on non-diachronic detection settings. We tested different training strategies to evaluate how the classification performance is affected by adding more data temporally distant from the test set and hence potentially different in terms of topic and language use. Our analysis points out the limits that a supervised classification model encounters on data that are heavily influenced by events. Our results show how AlBERTo is highly sensitive to the temporal distance of the fine-tuning set. However, with an adequate time window, the performance increases, while requiring less annotated data than a traditional classifier.

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A Novel Model for Imbalanced Data Classification

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.6145 ◽

2020 ◽

Vol 34 (04) ◽

pp. 6680-6687

Author(s):

Jian Yin ◽

Chunjing Gan ◽

Kaiqi Zhao ◽

Xuan Lin ◽

Zhe Quan ◽

...

Keyword(s):

Imbalanced Data ◽

Data Classification ◽

Classification Performance ◽

Classification Model ◽

Proposed Model ◽

Imbalanced Data Classification ◽

Public Datasets ◽

Distribution Cost ◽

Novel Model ◽

Learning Data

Recently, imbalanced data classification has received much attention due to its wide applications. In the literature, existing researches have attempted to improve the classification performance by considering various factors such as the imbalanced distribution, cost-sensitive learning, data space improvement, and ensemble learning. Nevertheless, most of the existing methods focus on only part of these main aspects/factors. In this work, we propose a novel imbalanced data classification model that considers all these main aspects. To evaluate the performance of our proposed model, we have conducted experiments based on 14 public datasets. The results show that our model outperforms the state-of-the-art methods in terms of recall, G-mean, F-measure and AUC.

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Shift-Invariance of Pattern Recognition in the Visual Field?

Perception ◽

10.1068/v96l0101 ◽

1996 ◽

Vol 25 (1_suppl) ◽

pp. 78-78 ◽

Cited By ~ 1

Author(s):

M Jüttner ◽

I Rentschler ◽

A Unzicker

Keyword(s):

Visual Field ◽

Feature Space ◽

Classification Performance ◽

Classification Model ◽

Perceptual Dimension ◽

Internal Representations ◽

Visual Hemifield ◽

Shift Invariance ◽

Hemispheric Specialisation ◽

The Individual

The classification behaviour of human observers with respect to compound Gabor signals was tested at foveal and extrafoveal retinal positions. Classification performance was analysed in terms of a probabilistic classification model recently proposed by Rentschler, Jüttner, and Caelli (1994 Vision Research34 669 – 687). The analysis allows inferences about structure and dimensionality of the individual internal representations underlying the classification task and their temporal evolution during the learning process. With this technique it was found that the internal representations of direct and eccentric viewing are intrinsically incommensurable in the sense that extrafoveal pattern representations are characterised by a lower perceptual dimension in feature space relative to the corresponding physical input signals, whereas foveal representations are not (Jüttner and Rentschler, 1996 Vision Research in press). We then addressed the question to what extent observers are capable of generalising class concepts that have been acquired at one particular retinal location to other retinal sites. We found partial generalisation with respect to spatial translation across the visual field. Moreover, there is, in the case of extrafoveal learning, a distinct asymmetry in performance with respect to the visual hemifield in which the signals were originally learned. The latter finding can be related to functional hemispheric specialisation in pattern learning and recognition.

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