Laplacian Support Vector Machine for Vibration-Based Robotic Terrain Classification

The achievement of robot autonomy has environmental perception as a prerequisite. The hazards rendered from uneven, soft and slippery terrains, which are generally named non-geometric hazards, are another potential threat reducing the traversing efficient, and therefore receiving more and more attention from the robotics community. In the paper, the vibration-based terrain classification (VTC) is investigated by taking a very practical issue, i.e., lack of labels, into consideration. According to the intrinsic temporal correlation existing in the sampled terrain sequence, a modified Laplacian SVM is proposed to utilise the unlabelled data to improve the classification performance. To the best of our knowledge, this is the first paper studying semi-supervised learning problem in robotic terrain classification. The experiment demonstrates that: (1) supervised learning (SVM) achieves a relatively low classification accuracy if given insufficient labels; (2) feature-space homogeneity based semi-supervised learning (traditional Laplacian SVM) cannot improve supervised learning’s accuracy, and even makes it worse; (3) feature- and temporal-space based semi-supervised learning (modified Laplacian SVM), which is proposed in the paper, could increase the classification accuracy very significantly.

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Financial Distress Prediction Based on Support Vector Machine with a Modified Kernel Function

Journal of Intelligent Systems ◽

10.1515/jisys-2014-0132 ◽

2016 ◽

Vol 25 (3) ◽

pp. 417-429

Author(s):

Chong Wu ◽

Lu Wang ◽

Zhe Shi

Keyword(s):

Support Vector Machine ◽

Kernel Function ◽

Financial Distress ◽

Classification Accuracy ◽

Feature Space ◽

Support Vector ◽

Input Space ◽

Financial Distress Prediction ◽

Support Vectors ◽

Distress Prediction

AbstractFor the financial distress prediction model based on support vector machine, there are no theories concerning how to choose a proper kernel function in a data-dependent way. This paper proposes a method of modified kernel function that can availably enhance classification accuracy. We apply an information-geometric method to modifying a kernel that is based on the structure of the Riemannian geometry induced in the input space by the kernel. A conformal transformation of a kernel from input space to higher-dimensional feature space enlarges volume elements locally near support vectors that are situated around the classification boundary and reduce the number of support vectors. This paper takes the Gaussian radial basis function as the internal kernel. Additionally, this paper combines the above method with the theories of standard regularization and non-dimensionalization to construct the new model. In the empirical analysis section, the paper adopts the financial data of Chinese listed companies. It uses five groups of experiments with different parameters to compare the classification accuracy. We can make the conclusion that the model of modified kernel function can effectively reduce the number of support vectors, and improve the classification accuracy.

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Finer Resolution Mapping of Marine Aquaculture Areas Using WorldView-2 Imagery and a Hierarchical Cascade Convolutional Neural Network

Remote Sensing ◽

10.3390/rs11141678 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1678 ◽

Cited By ~ 3

Author(s):

Yongyong Fu ◽

Ziran Ye ◽

Jinsong Deng ◽

Xinyu Zheng ◽

Yibo Huang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Contextual Information ◽

Feature Space ◽

Classification Performance ◽

Support Vector ◽

Marine Resources ◽

Marine Aquaculture ◽

Multi Scale ◽

Coastal Marine

Marine aquaculture plays an important role in seafood supplement, economic development, and coastal ecosystem service provision. The precise delineation of marine aquaculture areas from high spatial resolution (HSR) imagery is vital for the sustainable development and management of coastal marine resources. However, various sizes and detailed structures of marine objects make it difficult for accurate mapping from HSR images by using conventional methods. Therefore, this study attempts to extract marine aquaculture areas by using an automatic labeling method based on the convolutional neural network (CNN), i.e., an end-to-end hierarchical cascade network (HCNet). Specifically, for marine objects of various sizes, we propose to improve the classification performance by utilizing multi-scale contextual information. Technically, based on the output of a CNN encoder, we employ atrous convolutions to capture multi-scale contextual information and aggregate them in a hierarchical cascade way. Meanwhile, for marine objects with detailed structures, we propose to refine the detailed information gradually by using a series of long-span connections with fine resolution features from the shallow layers. In addition, to decrease the semantic gaps between features in different levels, we propose to refine the feature space (i.e., channel and spatial dimensions) using an attention-based module. Experimental results show that our proposed HCNet can effectively identify and distinguish different kinds of marine aquaculture, with 98% of overall accuracy. It also achieves better classification performance compared with object-based support vector machine and state-of-the-art CNN-based methods, such as FCN-32s, U-Net, and DeeplabV2. Our developed method lays a solid foundation for the intelligent monitoring and management of coastal marine resources.

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Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment

Sensors ◽

10.3390/s20226550 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6550 ◽

Cited By ~ 1

Author(s):

Chen Cheng ◽

Ji Chang ◽

Wenjun Lv ◽

Yuping Wu ◽

Kun Li ◽

...

Keyword(s):

Incremental Learning ◽

Classification Accuracy ◽

Concept Drift ◽

Dynamic Environment ◽

Temporal Correlation ◽

Classification Methods ◽

Terrain Classification ◽

Localization Accuracy ◽

Learning Framework ◽

Control Scheme

The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment.

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Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

Entropy ◽

10.3390/e21050519 ◽

2019 ◽

Vol 21 (5) ◽

pp. 519 ◽

Cited By ~ 6

Author(s):

Weibo Zhang ◽

Jianzhong Zhou

Keyword(s):

Diagnostic Algorithm ◽

Feature Space ◽

Vibration Signal ◽

Rolling Bearing ◽

Ensemble Empirical Mode Decomposition ◽

Classification Performance ◽

Fault Classification ◽

Permutation Entropy ◽

Support Vector ◽

Intrinsic Mode Functions

Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study. Firstly, the ensemble empirical mode decomposition algorithm (EEMD) was employed to adaptively decompose the vibration signal into multiple intrinsic mode functions (IMFs), and the representative IMFs which contained rich fault information were selected to reconstruct a feature vector space. Secondly, the multiscale permutation entropy (MPE) was used to calculate the complexity of reconstructed feature space. Finally, the value of multiscale permutation entropy was presented to a support vector machine for fault classification. The proposed diagnostic algorithm was applied to three groups of rolling bearing experiments. The experimental results indicate that the proposed method has better classification performance and robustness than other traditional methods.

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Improved semi-supervised learning technique for automatic detection of South African abusive language on Twitter

South African Computer Journal ◽

10.18489/sacj.v32i2.847 ◽

2020 ◽

Vol 32 (2) ◽

Author(s):

Oluwafemi Oriola ◽

Eduan Kotzé

Keyword(s):

Logistic Regression ◽

Supervised Learning ◽

South African ◽

Learning Curves ◽

Training Data ◽

Support Vector ◽

Learning Techniques ◽

Learning Technique ◽

Unlabelled Data ◽

Language Detection

Semi-supervised learning is a potential solution for improving training data in low-resourced abusive language detection contexts such as South African abusive language detection on Twitter. However, the existing semi-supervised learning methods have been skewed towards small amounts of labelled data, with small feature space. This paper, therefore, presents a semi-supervised learning technique that improves the distribution of training data by assigning labels to unlabelled data based on the majority voting over different feature sets of labelled and unlabelled data clusters. The technique is applied to South African English corpora consisting of labelled and unlabelled abusive tweets. The proposed technique is compared with state-of-the-art self-learning and active learning techniques based on syntactic and semantic features. The performance of these techniques with Logistic Regression, Support Vector Machine and Neural Networks are evaluated. The proposed technique, with accuracy and F1-score of 0.97 and 0.95, respectively, outperforms existing semi-supervised learning techniques. The learning curves show that the training data was used more efficiently by the proposed technique compared to existing techniques. Overall, n-gram syntactic features with a Logistic Regression classifier records the highest performance. The paper concludes that the proposed semi-supervised learning technique effectively detected implicit and explicit South African abusive language on Twitter.

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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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DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM

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

10.5194/isprsarchives-xl-1-w5-651-2015 ◽

2015 ◽

Vol XL-1-W5 ◽

pp. 651-656

Author(s):

F. Samadzadega ◽

H. Hasani

Keyword(s):

Urban Area ◽

Hyperspectral Imagery ◽

Feature Space ◽

Classification Performance ◽

Feature Subset Selection ◽

Bees Algorithm ◽

Support Vector ◽

Svm Classifier ◽

Lidar Data ◽

Feature Subset

Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performance especially in the complex urban area. In this paper feature level fusion of hyperspectral and LiDAR data is proposed where spectral and structural features are extract from both dataset, then hybrid feature space is generated by feature stacking. Support Vector Machine (SVM) classifier is applied on hybrid feature space to classify the urban area. In order to optimize the classification performance, two issues should be considered: SVM parameters values determination and feature subset selection. Bees Algorithm (BA) is powerful meta-heuristic optimization algorithm which is applied to determine the optimum SVM parameters and select the optimum feature subset simultaneously. The obtained results show the proposed method can improve the classification accuracy in addition to reducing significantly the dimension of feature space.

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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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A Novel Sparse Representation and Softmax Method for Human Activity Identification in Healthcare Systems

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3079 ◽

2020 ◽

Vol 10 (7) ◽

pp. 1724-1733

Author(s):

Youwei Yuan ◽

Wenpeng Tao ◽

Jintao Zhang ◽

Meilian Zheng ◽

Yao Yao ◽

...

Keyword(s):

Sparse Representation ◽

Human Activity ◽

Nearest Neighbor ◽

Feature Space ◽

Healthcare Systems ◽

Classification Performance ◽

Identification Accuracy ◽

Sensor Data ◽

Support Vector ◽

Research Attention

Human activity identification has been attracting extensive research attention due to its prominent applications in healthcare systems such as healthcare monitoring and rehabilitation process. Traditional methods are greatly dependent on hand-crafted feature extraction, hampering their generalization performance. In this research, a novel sparse representation and softmax (SRS) method is presented for human activity identification to reduce the computation complexity of the task and improve the accuracy of classification. The multi-class classifier based on the softmax function is firstly introduced to improve sensor data classification performance. Sparse representation technology is then applied in our work to extract human activity features from sensor data. The output of the classifier model, taking raw sensor data after transforming into a high-dimensional feature space as input, provides a normalization of the probability distribution of activity categories, thereby ensuring accuracy and efficiency under diverse human activities. Experiments on a collection of raw sensor data from wireless sensor networks demonstrate the identification accuracy of our approach compared with nearest neighbor, naive Bayesian classifier, and support vector machine methods. The F1-score of the proposed method is respectively 14.1%, 19.6%, and 6.8% higher than the approaches mentioned above, indicating the effectiveness of SRS.

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