Sample Selection Algorithm Research Articles

Molecular Reconstruction Method Based on NIR Spectroscopy for Reformates

With the increasing attention to environmental protection and strict national standards, the production of high-quality clean gasoline is more and more required in modern refineries. At present, the molecular-level online modeling of the gasoline blending process is rarely reported due to the lack of a proper characterization method. In this study, we have developed a molecular reconstruction method based on near-infrared (NIR) spectroscopy. We established a library that comprises an NIR spectrum and molecular composition of known reformates. We developed the sample selection algorithm that finds the sample with an NIR spectrum close to the target sample. The method-solving algorithm based on multiple linear regression was used to obtain the molecular composition of gasoline quickly. The method combines online measurement methods and molecular management technology to achieve online composition reconstruction. Seventy-five groups of reformates were collected to verify the feasibility of the method, and the results showed that the method predicted well.

Tripartite Graph Aided Tensor Completion For Sparse Network Measurement

Network measurements provide critical inputs for a wide range of network management. Existing network-wide monitoring methods face the challenge of incurring a high measurement cost. Some recent studies show that network-wide measurement data such as end-to-end latency and flow traffic, have hidden spatio-temporal correlations and thus low-rank features. Taking advantage of the low-rank feature, enlightened by tensor model's strong capability of information representation and extracting, this paper studies a novel sparse measurement scheduling problem which selects a proportion of Origin and Destination (OD) pairs to take measurements in the future time slots, while ensuring the data of the remaining un-measured OD pairs be accurately inferred through tensor completion. It is challenging to find the optimal sampling points (OD pairs) without knowing the structure of the future data and also infer the un-measured data in the presence of noise in the measurement samples. To conquer the challenges, we propose several techniques: a tripartite graph to illustrate the relationship between sample locations and tensor factorization, a graph-based sample selection algorithm, and a graph-based robust tensor completion algorithm. We have conducted extensive experiments based on two real network latency monitoring traces (PlanetLab and Harvard) and two other network monitoring traces (including a traffic trace Abilene and a throughput trace WS-Dream). Our results demonstrate that, even with a sampling ratio of less than 5%, our scheme can accurately obtain the complete network-wide monitoring data by inferring the missing ones based on the samples taken. To achieve similar recovery performance, the best peer tensor completion algorithm needs a significantly larger number of samples, with the sampling ratio up to 25-150 times ours.

AC-WGAN-GP: Generating Labeled Samples for Improving Hyperspectral Image Classification with Small-Samples

The lack of labeled samples severely restricts the classification performance of deep learning on hyperspectral image classification. To solve this problem, Generative Adversarial Networks (GAN) are usually used for data augmentation. However, GAN have several problems with this task, such as the poor quality of the generated samples and an unstable training process. Thereby, knowing how to construct a GAN to generate high-quality hyperspectral training samples is meaningful for the small-sample classification task of hyperspectral data. In this paper, an Auxiliary Classifier based Wasserstein GAN with Gradient Penalty (AC-WGAN-GP) was proposed. The framework includes AC-WGAN-GP, an online generation mechanism, and a sample selection algorithm. The proposed method has the following distinctive advantages. First, the input of the generator is guided by prior knowledge and a separate classifier is introduced to the architecture of AC-WGAN-GP to produce reliable labels. Second, an online generation mechanism ensures the diversity of generated samples. Third, generated samples that are similar to real data are selected. Experiments on three public hyperspectral datasets show that the generated samples follow the same distribution as the real samples and have enough diversity, which effectively expands the training set. Compared to other competitive methods, the proposed framework achieved better classification accuracy with a small number of labeled samples.

Disease-Ligand Identification Based on Flexible Neural Tree.

In order to screen the disease-related compounds of a traditional Chinese medicine prescription in network pharmacology research accurately, a new virtual screening method based on flexible neural tree (FNT) model, hybrid evolutionary method and negative sample selection algorithm is proposed. A novel hybrid evolutionary algorithm based on the Grammar-guided genetic programming and salp swarm algorithm is proposed to infer the optimal FNT. According to hypertension, diabetes, and Corona Virus Disease 2019, disease-related compounds are collected from the up-to-date literatures. The unrelated compounds are chosen by negative sample selection algorithm. ECFP6, MACCS, Macrocycle, and RDKit are utilized to numerically characterize the chemical structure of each compound collected, respectively. The experiment results show that our proposed method performs better than classical classifiers [Support Vector Machine (SVM), random forest (RF), AdaBoost, decision tree (DT), Gradient Boosting Decision Tree (GBDT), KNN, logic regression (LR), and Naive Bayes (NB)], up-to-date classifier (gcForest), and deep learning method (forgeNet) in terms of AUC, ROC, TPR, FPR, Precision, Specificity, and F1. MACCS method is suitable for the maximum number of classifiers. All methods perform poorly with ECFP6 molecular descriptor.

UNSUPERVISED SAR CHANGE DETECTION METHOD BASED ON REFINED SAMPLE SELECTION

Abstract. In deep learning based synthetic aperture radar (SAR) change detection, selecting samples of high quality is a crucial step. In this work, we have proposed a refined sample selection algorithm for unsupervised SAR change detection. The propose and incorporation of volume control factors and multi-hierarchical fuzzy c-means (MH-FCM) algorithm generate samples of large diversity and high confidence, thus satisfying the needs for high quality samples. The method includes two phases: firstly, an enhanced difference image is constructed according to the difference consistency between single pixels and their neighbourhoods, and a triangular threshold segmentation method is then proposed to determine the volume control factors for sample selection. MH-FCM is developed to classify the log mean ratio difference image into 4 classes. Secondly, a dual-channel convolution neural network with an adaptive weighted loss is adopted to learn and predict the input and to obtain the change detection result. Experimental results of the Gaofen-3 dataset in Beijing have validated the effectiveness and usefulness of the proposed method.

An active learning approach for clustering single-cell RNA-seq data

AbstractSingle-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover undiscovered cell types. Most methods for clustering scRNA-seq data use an unsupervised learning strategy. Since the clustering step is separated from the cell annotation and labeling step, it is not uncommon for a totally exotic clustering with poor biological interpretability to be generated—a result generally undesired by biologists. To solve this problem, we proposed an active learning (AL) framework for clustering scRNA-seq data. The AL model employed a learning algorithm that can actively query biologists for labels, and this manual labeling is expected to be applied to only a subset of cells. To develop an optimal active learning approach, we explored several key parameters of the AL model in the experiments with four real scRNA-seq datasets. We demonstrate that the proposed AL model outperformed state-of-the-art unsupervised clustering methods with less than 1000 labeled cells. Therefore, we conclude that AL model is a promising tool for clustering scRNA-seq data that allows us to achieve a superior performance effectively and efficiently.Active learning (AL) model is a framework designed for single-cell RNA sequence (scRNA-seq) clustering. This model requires that the researchers label a small number of cells selected by a sample selection algorithm. The labeled cells are then used for the supervision of the clustering, to significantly boost the clustering performance of scRNA-seq.

Hypergraph regularized semi-supervised support vector machine

At present, graph regularized semi-supervised methods achieve excellent performance in various fields. However, the manifold regularization term of most methods only considers the pairwise relationship between data, thus it cannot accurately represent the multivariate and complex structure of data. In this paper, we exploit the multivariate manifold structure by hypergraph, and propose a hypergraph regularized semi-supervised support vector machine (HGSVM) algorithm. To accelerate the training process of HGSVM, we further develop a fast algorithm based boundary sample selection algorithm, termed fast-HGSVM. Moreover, two SMOTE-variant techniques and the one-vs-rest strategy are introduced in fast-HGSVM, and two multi-category semi-supervised algorithms called fast-ASHGSVM and fast-KSHGSVM are proposed. Experiments on two moons and UCI datasets validate the effectiveness of the proposed algorithms.

Ensemble classification for intrusion detection via feature extraction based on deep Learning

An intrusion detection system is a security system that aims to detect sabotage and intrusions on networks to inform experts of the attack and abuse of the network. Different classification methods have been used in the intrusion detection systems such as fuzzy, genetic algorithms, decision trees, artificial neural networks, and support vector machines. Moreover, ensemble classifiers have shown more robust and effective performance for various tasks in the field. In this paper, we adopt ensemble models in order to improve the performance of intrusion detection and, at the same time, decrease the false alarm rate. We use kNN for multi-class classification, as well as SVM to approach the classification problem in normal-based detection. In order to combine multiple outputs, we use the Dempster–Shafer method in which there is the possibility of explicit retrieval of uncertainty. Moreover, we utilize deep learning for extracting features to train the samples, selected by the sample selection algorithm based on ensemble margin. We compare our results with state-of-the-art methods on benchmarking datasets such as UNSW-NB15, CICIDS2017, and NSL-KDD. Our proposed method indicates the superiority in terms of prominent metrics Accuracy, Precision, Recall, and F-measure.

A sample selection method specific to unknown test samples for calibration and validation sets based on spectra similarity

As is known to all, the construction of calibration and validation sets is of great importance for how to select representative samples into subsets so that the calibration model can be built, evaluated and predicted effectively for model development. In this study, a method was proposed for the calibration and validation sets constructed by selecting samples maximally similar to the test samples based on the spectra data. Both the Euclidean distance and Mahalanobis distance were attempted to estimate the spectra similarity. The method to select samples for calibration is more suitable and specific to unknown test samples in practical applications, thus improving the measurement accuracy. In addition, the optimization of calibration set size was carried out to avoid the influence of unnecessary samples. Two data sets of Salvia miltiorrhiza (S. miltiorrhiza) and corn by near infrared spectroscopy (NIR) were used to test the performance of the proposed method compared with two typical sample-selection algorithms, Kennard-Stone (KS) and sample set partitioning based on joint x–y distances (SPXY). The experimental results indicated that the proposed method could select a more targeted set of samples for the unknown test samples and had the superior predictive performance to the KS and SPXY methods.

Comparison of a novel PLS1-DA, traditional PLS2-DA and assigned PLS1-DA for classification by molecular spectroscopy

Partial least square discriminant analysis (PLS-DA) has achieved a huge success in many research systems, such as quality evaluation of agricultural products and drug analysis. However, it can be found that PLS-DA has various forms which can be mainly divided into two strategies separately called as PLS1-DA and PLS2-DA which is overused. In this work, we proposed a novel strategy not only to select representative samples for modeling, but also to build qualitative models, which is called as principal component analysis Euclidean distance PLS1-DA (PCA-EuD-PLS1-DA). Outliers were detected by the method Leverage. It is very interesting to compare the results of traditional PLS-DA tactics with the proposed method on six datasets. EuD-PLS1-DA with PCA partitioning subsets has the optimal performances than both PLS2-DA and assigned PLS1-DA when confronting multi-class problems. In particular, different classes’ tablets in NIR can be well discriminated and different classes’ tablets in Raman can be discriminated in all EuD-PLS1-DA models; and their prediction accuracy is above 98% and 80%, respectively. Their prediction accuracy for PLS2-DA is above 56% and 26%, respectively. And all RMSECV in EuD-PLS1-DA is smaller than the remaining classifiers; so does RMSEP. This behavior means EuD-PLS1-DA has a better fitness than the remaining methods. When there are only two classes in a dataset, performance of all PLS-DA models is highly similar. Before comparing the results of PLS-DA derivatives, the shortcomings in the other two methods have been systematically described. Sample selection algorithms including K–S, SPXY and PCA were compared with percentage of chosen samples in every class. And the current ratios are mainly in the area of 70–90%. Besides, ratios of every class’ samples selected for modeling are highly similar among K–S, SPXY and PCA. Most importantly, the proposed method can be realized in most statistical software.

Cyclic training sample selection and cancellation technique for airborne STAP radar under nonhomogeneous environment

When training samples contain interference target signals (i.e., outliers), the performance of space–time adaptive processing (STAP) will be degraded. To solve this problem, a cyclic training sample selection and cancellation (CTSSC) algorithm based on sample matrix inversion (SMI) statistic is proposed. This algorithm consists of three steps. Firstly, a cyclic process is carried out to detect outliers. In each detection cycle, the sample matrix is estimated by all the training samples contained in the training sample set, and the outliers with relatively high jamming-to-noise ratio (JNR) will be detected. Then the corresponding training samples are removed from the training sample set and the sample matrix is updated. This cycle continues until the SMI statistics of all the remaining training samples do not exceed the detection threshold. Secondly, for each outlier detected by the first step, it will be reconstructed by the estimated complex amplitude and steering vector. Then the outlier will be cancelled by the corresponding reconstructed outlier. Finally, conventional STAP will be carried out to suppress clutter. Simulation results validate the effectiveness of the CTSSC algorithm with measured data.

An Online Virtual Metrology Model With Sample Selection for the Tracking of Dynamic Manufacturing Processes With Slow Drift

Modeling of dynamic manufacturing processes with slow drift using data-driven approaches is challenging because most data-driven models are trained by off-line data. In this paper, we propose to track slow drift of manufacturing processes using an online Bayesian Auto-Regression eXogenous (ARX) model with time-variant parameters. The model parameters are trained off-line and are updated online using Bayes’ rule. To avoid the frequent online model update, a Sample Importance (SI) test is proposed to screen online samples and only the sample that passes SI test is incremented to the VM model. The SI test decides the importance of the sample by collectively considering sample freshness, model prediction error and prediction uncertainty. Furthermore, the SI test is applied to off-line Data Base (DB) and an iteration algorithm is devised for off-line sample selection. The off-line sample selection algorithm can effectively reduce the sample redundancy in off-line DB while maintaining the model performance. To validate the effectiveness of the proposed method, Prognostics and Health Management (PHM) data challenge 2016 dataset is employed to predict material removal rate of chemical-mechanical planarization process. The validation results indicate that the proposed method outperforms existing state-of-art approaches in literature such as Just-in-Time (JIT) and deep learning models.

Sample selection algorithms for credit risk modelling through data mining techniques

Sample selection algorithms for credit risk modelling through data mining techniques

Sample selection algorithms for credit risk modelling through data mining techniques

Credit risk assessment is a very challenging and important problem in the domain of financial risk management. The development of reliable credit rating/scoring models is of paramount importance in this area. There are different algorithms and approaches for constructing such models to classify credit applicants (firms or individuals) into risk classes. Reliable sample selection is crucial for this task. The aim of this paper is to examine the effectiveness of sample selection schemes in combination with different classifiers for constructing reliable default prediction models. We consider different algorithms to select representative cases and handle class imbalances. Empirical results are reported for a dataset of Greek companies from the commercial sector.

Combining speech sample and feature bilateral selection algorithm for classification of Parkinson's disease

Diagnosis of Parkinson's disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.

Active Learning for Classification with Maximum Model Change

Most existing active learning studies focus on designing sample selection algorithms. However, several fundamental problems deserve investigation to provide deep insight into active learning. In this article, we conduct an in-depth investigation on active learning for classification from the perspective of model change. We derive a general active learning framework for classification called maximum model change (MMC), which aims at querying the influential examples. The model change is quantified as the difference between the model parameters before and after training with the expanded training set. Inspired by the stochastic gradient update rule, the gradient of the loss with respect to a given candidate example is adopted to approximate the model change. This framework is applied to two popular classifiers: support vector machines and logistic regression. We analyze the convergence property of MMC and theoretically justify it. We explore the connection between MMC and uncertainty-based sampling to provide a uniform view. In addition, we discuss its potential usability to other learning models and show its applicability in a wide range of applications. We validate the MMC strategy on two kinds of benchmark datasets, the UCI repository and ImageNet, and show that it outperforms many state-of-the-art methods.

Query expansion for object retrieval with active learning using BoW and CNN feature

Most effective particular object and image retrieval approaches are based on the bag-of-words (BoW) model, and all state-of-the-art performance mainly involves a query expansion procedure, which is able to significantly improve retrieval results. Nowadays, Convolutional Neural Network(CNN) is widely applied in computer vision field, including image classification, caption, recognition and retrieval, etc. We introduce an extension to query expansion: an automatic method to select good candidate samples for interactive annotation which is used in query expansion using both BoW method and CNN feature. In this work, we address the query expansion framework using active learning, where the main focus is on the sample selection step in the process of query expansion. More specifically, we propose an active sample selection algorithm based on binary relevance classification, based on the assumption that most confusing samples of the classifiers have high probability to contain helpful true positives for query expansion, which significantly improves the retrieval performance. It takes full use of the multimodal information of the shortlist obtained from the basic retrieval to train a binary relevance classifier, which is used to pick up the most confusing samples for human annotation, with top list as unlabeled data and bottom list as fake negatives. And it can achieve a faster and better retrieval than naive top sample selection method. We also fuse BoW vector and CNN prediction in the retrieval system for a better performance. To evaluate the performance of our proposed method, experiments are conducted on Standard Oxford (5K and 105K) and Paris (6K) datasets, and experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed method.

Stimuli-Magnitude-Adaptive Sample Selection for Data-Driven Haptic Modeling

Data-driven haptic modeling is an emerging technique where contact dynamics are simulated and interpolated based on a generic input-output matching model identified by data sensed from interaction with target physical objects. In data-driven modeling, selecting representative samples from a large set of data in a way that they can efficiently and accurately describe the whole dataset has been a long standing problem. This paper presents a new algorithm for the sample selection where the variances of output are observed for selecting representative input-output samples in order to ensure the quality of output prediction. The main idea is that representative pairs of input-output are chosen so that the ratio of the standard deviation to the mean of the corresponding output group does not exceed an application-dependent threshold. This output- and standard deviation-based sample selection is very effective in applications where the variance or relative error of the output should be kept within a certain threshold. This threshold is used for partitioning the input space using Binary Space Partitioning-tree (BSP-tree) and k-means algorithms. We apply the new approach to data-driven haptic modeling scenario where the relative error of the output prediction result should be less than a perceptual threshold. For evaluation, the proposed algorithm is compared to two state-of-the-art sample selection algorithms for regression tasks. Four kinds of haptic related behavior–force datasets are tested. The results showed that the proposed algorithm outperformed the others in terms of output-approximation quality and computational complexity.

Sample selection for visual domain adaptation via sparse coding

Visual domain adaptation has attracted much attention and has made great achievement in recent years. It deals with the problem of distribution divergence between source and target domains. Current methods mostly focus on transforming images from different domains into a common space to minimize the distribution divergence. However, there are many irrelevant source samples for target domain even after the transformation. In order to eliminate the irrelevant samples, we develop a sample selection algorithm using sparse coding theory. We do the sample selection in a common subspace of source and target data to find as many as relevant source samples. In the common subspace, data characteristics are preserved by using graph regularization. Therefore, we can select the most relevant samples for our target image classification task. Moreover, in order to build a discriminative classifier for the target domain, we use not only the common part of source and target domains learned in the common subspace but also the specific part of target domain. The algorithm can be extended to handle samples from multiple source domains. Experimental results show that our visual domain adaptation method on the image classification tasks can be very effective for the state-of-the-art datasets.

A Novel Sample Selection Strategy for Imbalanced Data of Biomedical Event Extraction with Joint Scoring Mechanism.

Biomedical event extraction is an important and difficult task in bioinformatics. With the rapid growth of biomedical literature, the extraction of complex events from unstructured text has attracted more attention. However, the annotated biomedical corpus is highly imbalanced, which affects the performance of the classification algorithms. In this study, a sample selection algorithm based on sequential pattern is proposed to filter negative samples in the training phase. Considering the joint information between the trigger and argument of multiargument events, we extract triplets of multiargument events directly using a support vector machine classifier. A joint scoring mechanism, which is based on sentence similarity and importance of trigger in the training data, is used to correct the predicted results. Experimental results indicate that the proposed method can extract events efficiently.