Generation Of Synthetic Samples Research Articles

CARBO: Clustering and rotation based oversampling for class imbalance learning

Class imbalance of a data set is a crucial problem in machine learning where one class significantly outnumbers others. In such a data set, classification is a troublesome task for the standard classification algorithms, leading to bias towards the majority class. Different methods have been developed so far, such as oversampling, undersampling, and cost-sensitive learning, to deal with class imbalance circumstances. Among these techniques, oversampling technique does not suffer from the information loss and critical cost selection challenges. However, appropriate synthetic sample generation can be challenging and vulnerable to privacy leakage. This research proposed an oversampling technique, called CARBO, using threshold-based geometric rotation and majority class influenced clustering. Unlike the existing resampling approaches to class imbalance problem, we contribute to consider the data privacy and optimal sample generation together for effective oversampling. The performance of CARBO is evaluated using 44 benchmark imbalanced data set. The empirical analysis elucidates that CARBO can make boosting-based C4.5 ensemble classifiers perform higher for 73% of the data set than six state-of-the-art approaches. In addition, the theoretical compatibility analysis of CARBO demonstrates its robustness.

Addressing imbalance in graph datasets: Introducing GATE-GNN with graph ensemble weight attention and transfer learning for enhanced node classification

Significant challenges arise when Graph Neural Networks (GNNs) try to deal with uneven data. Specifically in signed and weighted graph structures. This makes classification tasks less effective. Within the GNN context, researchers have found traditional solutions like resampling, reweighting, and synthetic sample generation to be inadequate. GATE-GNN, a novel architecture designed specifically for imbalanced datasets, overcomes these limitations. GATE-GNN integrates an ensemble of network modules that harness the spatial features of graph networks and effectively utilise embedding information from earlier layers. This unique approach not only bolsters generalisation by reducing volatility. It also refines the optimisation algorithm, resulting in more accurate and stable classification outcomes. We rigorously tested the effectiveness of GATE-GNN on four widely recognised datasets: Cora, NELL, Citeseer, and PubMed. We performed a comparative analysis against established methods such as Graph Convolutional Networks (GCN), Graph Sample and Aggregate (GraphSAGE), Propagation Multilayer Perceptron PMLP), Imbalanced Node Sampling GNN (INS-GNN), GNN-Curriculum Learning (GNN-CL) and Graph Attention Networks (GAT). Empirical results demonstrate that GATE-GNN significantly outperforms these existing models, achieving an average improvement in classification accuracy of approximately 5%–10% over the previous best results. Additionally, GATE-GNN presents a marked reduction in training time. This underscores its efficiency and suitability for practical applications in imbalanced graph data scenarios. Implementation of the proposed GATE-GNN can be accessed here https://github.com/afofanah/GATE-GNN.

A generation of synthetic samples and artificial outliers via principal component analysis and evaluation of predictive capability in binary classification models

Unbalanced sample groups tend to yield models with a higher prevalence of predominant classes. A sample group with balanced classes contributes to the development of more robust models with improved predictive capability to classify classes equally. In the literature, two methodologies for sample balancing can be found: elimination (undersampling) and synthetic sample generation (oversampling). Undersampling methodologies result in the loss of real samples, while oversampling methods may introduce issues related to adding non-real signals to the original spectra. To overcome these challenges, this paper aimed to utilize Principal Component Analysis (PCA) for the generation of virtual samples (synthetic samples and artificial outliers) to balance data in multivariate classification models. The proposed methodology was applied to data from mid-infrared spectroscopy (MIR) and high-resolution mass spectrometry (HRMS) with Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) models. The constructed models demonstrate that the addition of virtual samples enhances performance parameters (e.g., false negative rate, false positive rate, accuracy, sensitivity, specificity, among others) compared to unbalanced models, while also mitigating overfitting (a problem found in unbalanced models). Performance parameters exhibited a more significant improvement percentage using the non-linear model (SVM) compared to the linear model (PLS-DA). Furthermore, the created virtual spectra do not introduce new signals, i.e., original, and virtual spectra exhibit a similar spectral profile, differing only in the intensity levels. Finally, all models demonstrated good predictive capability according to permutation testing for the binary model developed in this work, limiting the rate of class permutation retention (between 40 % and 60 % of the y-vector remained in the original class). All created models exhibited accuracy values higher than the accuracy distribution of models with permuted classes for the test group.

Effectiveness of Data Imbalance Treatment in Weather-Related Crash Severity Analysis

Accurate predictive modeling is often hindered by the prevalent issue of class imbalance within weather-related crash datasets. To address this critical challenge, this study introduces a novel and tailored synthetic data generation technique aimed at effectively handling nominal predictors specific to weather-related crash cases in North Carolina. Data treatment techniques such as the synthetic minority over-sampling technique-nominal (SMOTE-N) and adaptive synthetic-nominal (ADASYN-N) were investigated in this study. A comprehensive comparison of these data treatment techniques is conducted using two prominent machine learning models: the bagging algorithm (random forest [RF]) and the boosting algorithm (extreme gradient boosting [XGBoost]). The findings indicate that the effectiveness of data treatment varies with the severity level and the algorithm used. The ADASYN-N technique was observed to be highly effective for severe and moderate injury crash prediction using both the RF and XGBoost models, while the control dataset and SMOTE-N demonstrated notable performance in property damage only crash prediction using both the RF and XGBoost models. The findings from evaluating the performance of these models with data treatment methods serve as a benchmark for practitioners in selecting appropriate synthetic sample generation techniques, consequently facilitating the development of more accurate crash severity prediction models and contributing to enhanced traffic safety strategies.

Clustering-based incremental learning for imbalanced data classification

Imbalanced data classification presents a significant challenge when there is a substantial disparity in sample sizes across different classes. This issue severely affects classifier accuracy in predicting minority classes, hampering numerous real-world applications. Traditional methods address data imbalance by using undersampling or oversampling techniques. However, these methods may lead to information loss during sample reduction or introduce noise and model bias through synthetic sample generation. In this paper, we introduce DRIL, an innovative clustering-based incremental learning approach designed to overcome these limitations and improve the classification of minority class samples. Specifically, we employ a “two-step clustering” method to rebalance the dataset, partitioning it into similar and representative sub-dataset. Subsequently, incremental learning is applied to enable the classifier to gradually acquire knowledge about these sub-data, establishing a comprehensive understanding of all features present in the imbalanced dataset. Experimental results on twenty datasets demonstrate that our incremental learning-based algorithm outperforms baseline methods in correctly classifying minority classes while exhibiting improved precision and F1 score performance.

Advanced deep learning approach for enhancing crop disease detection in agriculture using hyperspectral imaging

Crop diseases pose significant challenges to global food security and agricultural sustainability. Timely and accurate disease detection is crucial for effective disease management and minimizing crop losses. In recent years, hyperspectral imaging has emerged as a promising technology for non-destructive and early disease detection in crops. This research paper presents an advanced deep learning approach for enhancing crop disease detection using hyperspectral imaging. The primary objective is to propose a hybrid Autoencoder-Generative Adversarial Network (AE-GAN) model that effectively extracts meaningful features from hyperspectral images and addresses the limitations of existing techniques. The hybrid AE-GAN model combines the strengths of the Autoencoder for feature extraction and the Generative Adversarial Network for synthetic sample generation. Through extensive evaluation, the proposed model outperforms existing techniques, achieving exceptional accuracy in crop disease detection. The results demonstrate the superiority of the hybrid AE-GAN model, offering substantial advantages in terms of feature extraction, synthetic sample generation, and utilization of spatial and spectral information. The proposed model’s contributions to sustainable agriculture and global food security make it a valuable tool for advancing agricultural practices and enhancing crop health monitoring. With its promising implications, the hybrid AE-GAN model represents a significant advancement in crop disease detection, paving the way for a more resilient and food-secure future.

Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data

This study introduces a sophisticated intrusion detection system (IDS) that has been specifically developed for internet of things (IoT) networks. By utilizing the capabilities of long short-term memory (LSTM), a deep learning model renowned for its proficiency in modeling sequential data, our intrusion detection system (IDS) effectively discerns between regular network traffic and potential malicious attacks. In order to tackle the issue of imbalanced data, which is a prevalent concern in the development of intrusion detection systems (IDSs), we have integrated the synthetic minority over-sampling technique (SMOTE) into our approach. This incorporation allows our model to accurately identify infrequent incursion patterns. The rebalancing of the dataset is accomplished by SMOTE through the generation of synthetic samples belonging to the minority class. Various strategies, such as the utilization of generative adversarial networks (GANs), have been put forth in order to tackle the issue of data imbalance. However, SMOTE (synthetic minority over-sampling technique) presents some distinct advantages when applied to intrusion detection. The SMOTE is characterized by its simplicity and proven efficacy across diverse areas, including in intrusion detection. The implementation of this approach is straightforward and does not necessitate intricate adversarial training techniques such as generative adversarial networks (GANs). The interpretability of SMOTE lies in its ability to generate synthetic samples that are aligned with the properties of the original data, rendering it well suited for security applications that prioritize transparency. The utilization of SMOTE has been widely embraced in the field of intrusion detection research, demonstrating its effectiveness in augmenting the detection capacities of intrusion detection systems (IDSs) in internet of things (IoT) networks and reducing the consequences of class imbalance. This study conducted a thorough assessment of three commonly utilized public datasets, namely, CICIDS2017, NSL-KDD, and UNSW-NB15. The findings indicate that our LSTM-based intrusion detection system (IDS), in conjunction with the implementation of SMOTE to address data imbalance, outperforms existing methodologies in accurately detecting network intrusions. The findings of this study provide significant contributions to the domain of internet of things (IoT) security, presenting a proactive and adaptable approach to safeguarding against advanced cyberattacks. Through the utilization of LSTM-based deep learning techniques and the mitigation of data imbalance using SMOTE, our AI-driven intrusion detection system (IDS) enhances the security of internet of things (IoT) networks, hence facilitating the wider implementation of IoT technologies across many industries.

ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets

Oversampling is commonly used to improve classifier performance for small tabular imbalanced datasets. State-of-the-art linear interpolation approaches can be used to generate synthetic samples from the convex space of the minority class. Generative networks are common deep learning approaches for synthetic sample generation. However, their scope on synthetic tabular data generation in the context of imbalanced classification is not adequately explored. In this article, we show that existing deep generative models perform poorly compared to linear interpolation-based approaches for imbalanced classification problems on small tabular datasets. To overcome this, we propose a deep generative model, ConvGeN that combines the idea of convex space learning with deep generative models. ConvGeN learns coefficients for the convex combinations of the minority class samples, such that the synthetic data is distinct enough from the majority class. Our benchmarking experiments demonstrate that our proposed model ConvGeN improves imbalanced classification on such small datasets, as compared to existing deep generative models, while being on par with the existing linear interpolation approaches. Moreover, we discuss how our model can be used for synthetic tabular data generation in general, even outside the scope of data imbalance, and thus improves the overall applicability of convex space learning.

Bone-GAN: Generation of virtual bone microstructure of high resolution peripheral quantitative computed tomography.

Data-driven development of medical biomarkers of bone requires a large amount of image data but physical measurements are generally too restricted in size and quality to perform a robusttraining. This study aims to provide a reliable in silico method for the generation of realistic bone microstructure with defined microarchitectural properties. Synthetic bone samples may improve training of neural networks and serve for the development of new diagnostic parameters of bone architecture andmineralization. One hundred-fifty cadaveric lumbar vertebrae from 48 different male human spines were scanned with a high resolution peripheral quantitative CT. After prepocessing the scans, we extracted 10,795 purely spongeous bone patches, each with a side length of 32 voxels (5 mm) and isotropic voxel size of 164μm. We trained a volumetric generative adversarial network (GAN) in a progressive manner to create synthetic microstructural bone samples. We then added a style transfer technique to allow the generation of synthetic samples with defined microstructure and gestalt by simultaneously optimizing two entangled loss functions. Reliability testing was performed by comparing real and synthetic bone samples on 10 well-understood microstructuralparameters. The method was able to create synthetic bone samples with visual and quantitative properties that effectively matched with the real samples. The GAN contained a well-formed latent space allowing to smoothly morph bone samples by their microstructural parameters, visual appearance or both. Optimum performance has been obtained for bone samples with voxel size 32 × 32 × 32, but also samples of size 64 × 64 × 64 could be synthesized. Our two-step-approach combines a parameter-agnostic GAN with a parameter-specific style transfer technique. It allows to generate an unlimited anonymous database of microstructural bone samples with sufficient realism to be used for the development of new data-driven methods of bone-biomarkers. Particularly, the style transfer technique can generate datasets of bone samples with specific conditions to simulate certain bonepathologies.

An artificial intelligence-based decision support system for early diagnosis of polycystic ovaries syndrome

Polycystic ovary syndrome (PCOS) can affect a female’s reproductive system and comes with associated complications to the endocrine system of the affected individual. The diagnosis success of the condition varies depending on the stage of the disease. Thus, there is a need for investment in additional technologies that can help bolster the overall diagnosis success of the condition and to cue in prompt care strategies. A substantial amount of work has been done on this, where artificial intelligence technology has been investigated around the exploitation of patient medical health records towards predicting whether a patient is carrying the PCOS condition. The shortcomings associated with this related literature are based on the use of an unbalanced dataset towards the training of the candidate models, which can induce a form of model bias and approach the problem as a binary-based prediction exercise. This study aims at providing a solution to this apparent gap in knowledge by designing a prediction model using the Kaggle PCOS dataset, which is initially balanced using a synthetic sample generation algorithm. Next, a probability-based inference system is designed to estimate and stage the degree of the PCOS condition in the patient.

ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning.

In recent years, cybersecurity has been strengthened through the adoption of processes, mechanisms and rapid sources of indicators of compromise in critical areas. Among the most latent challenges are the detection, classification and eradication of malware and Denial of Service Cyber-Attacks (DoS). The literature has presented different ways to obtain and evaluate malware- and DoS-cyber-attack-related instances, either from a technical point of view or by offering ready-to-use datasets. However, acquiring fresh, up-to-date samples requires an arduous process of exploration, sandbox configuration and mass storage, which may ultimately result in an unbalanced or under-represented set. Synthetic sample generation has shown that the cost associated with setting up controlled environments and time spent on sample evaluation can be reduced. Nevertheless, the process is performed when the observations already belong to a characterized set, totally detached from a real environment. In order to solve the aforementioned, this work proposes a methodology for the generation of synthetic samples of malicious Portable Executable binaries and DoS cyber-attacks. The task is performed via a Reinforcement Learning engine, which learns from a baseline of different malware families and DoS cyber-attack network properties, resulting in new, mutated and highly functional samples. Experimental results demonstrate the high adaptability of the outputs as new input datasets for different Machine Learning algorithms.

Multiuser Physical-Layer Authentication Based on Latent Perturbed Neural Networks for Industrial Internet of Things

Recently, learning (DL)-based physical-layer authentication (PLA) has attracted much attention since artificial neural networks (ANNs) can be built to extract useful features from complex wireless environments, thus achieving high authentication performance and lightweight deployment in mobile edge computing (MEC)-Industrial Internet of Things (IIoT) scenario. However, the low latency characteristic of MEC makes it impossible to have much time to obtain sufficient signals for training the authentication system, which will cause over-fitting issues and deteriorate the authentication performance. Data augmentation is an effective method to address this problem. However, existing PLA with data augmentation can not generate representative and high-quality samples, consequently lacking generality in the actual identity authentication. To tackle this problem, a novel channel impulse response (CIR)-based multiuser authentication named latent perturbed neural networks (LPNNs) is proposed in this article, aiming at achieving high authentication performance even when trained a few data. Instead of relying on the generation of synthetic samples, the proposed LPNN adds Gaussian noise in the smooth latent space to avoid underdetermined and poor generalization, which has better interpretability. Specifically, to obtain a better understanding than a black box that connects input CIRs to authentication results, we defined Fingerprint Library and provided post-hoc explanations to answer the following question: which library examples explain the authentication results issued for a given CIR sample? Moreover, the simulations under the static and dynamic IIoT scenarios verify the superiority in authentication accuracy of the proposed LPNN over vanilla deep neural network (DNN) and convolutional neural network (CNN).

Classification method for imbalanced LiDAR point cloud based on stack autoencoder

<abstract><p>The existing classification methods of LiDAR point cloud are almost based on the assumption that each class is balanced, without considering the imbalanced class problem. Moreover, from the perspective of data volume, the LiDAR point cloud classification should be a typical big data classification problem. Therefore, by studying the existing deep network structure and imbalanced sampling methods, this paper proposes an oversampling method based on stack autoencoder. The method realizes automatic generation of synthetic samples by learning the distribution characteristics of the positive class, which solves the problem of imbalance training data well. It only takes the geometric coordinates and intensity information of the point clouds as the input layer and does not need feature construction or fusion, which reduces the computational complexity. This paper also discusses the influence of sampling number, oversampling method and classifier on the classification results, and evaluates the performance from three aspects: true positive rate, positive predictive value and accuracy. The results show that the oversampling method based on stack autoencoder is suitable for imbalanced LiDAR point cloud classification, and has a good ability to improve the effect of positive class. If it is combined with optimized classifier, the classification performance of imbalanced point cloud is greatly improved.</p></abstract>

Zero-Inflated Patent Data Analysis Using Generating Synthetic Samples

Due to the expansion of the internet, we encounter various types of big data such as web documents or sensing data. Compared to traditional small data such as experimental samples, big data provide more chances to find hidden and novel patterns with big data analysis using statistics and machine learning algorithms. However, as the use of big data increases, problems also occur. One of them is a zero-inflated problem in structured data preprocessed from big data. Most count values are zeros because a specific word is found in only some documents. In particular, since most of the patent data are in the form of a text document, they are more affected by the zero-inflated problem. To solve this problem, we propose a generation of synthetic samples using statistical inference and tree structure. Using patent document and simulation data, we verify the performance and validity of our proposed method. In this paper, we focus on patent keyword analysis as text big data analysis, and we encounter the zero-inflated problem just like other text data.

HSDP: A Hybrid Sampling Method for Imbalanced Big Data Based on Data Partition

The classical classifiers are ineffective in dealing with the problem of imbalanced big dataset classification. Resampling the datasets and balancing samples distribution before training the classifier is one of the most popular approaches to resolve this problem. An effective and simple hybrid sampling method based on data partition (HSDP) is proposed in this paper. First, all the data samples are partitioned into different data regions. Then, the data samples in the noise minority samples region are removed and the samples in the boundary minority samples region are selected as oversampling seeds to generate the synthetic samples. Finally, a weighted oversampling process is conducted considering the generation of synthetic samples in the same cluster of the oversampling seed. The weight of each selected minority class sample is computed by the ratio between the proportion of majority class in the neighbors of this selected sample and the sum of all these proportions. Generation of synthetic samples in the same cluster of the oversampling seed guarantees new synthetic samples located inside the minority class area. Experiments conducted on eight datasets show that the proposed method, HSDP, is better than or comparable with the typical sampling methods for F-measure and G-mean.

Intelligent Fault Diagnosis of Rotary Machines: Conditional Auxiliary Classifier GAN Coupled With Meta Learning Using Limited Data

The industrial advancement has promoted the development of deep learning (DL)-based intelligent fault diagnosis methods for condition-based maintenance (CBM). Though these methods rely on large dataset for training, the collection of large number of fault samples is not practically feasible. For this purpose, generative adversarial networks (GANs) are capable to generate high-quality synthetic samples. However, the problem still persists with the training of GAN using limited fault samples that are present in practical conditions. This article proposes a novel conditional auxiliary classifier GAN framework coupled with model agnostic meta learning (MAML) to resolve this problem. The objective is to initialize and update the network parameters using MAML instead of regular stochastic gradient learning. This modification enables GAN to learn the task of synthetic sample generation using the limited training dataset. The effectiveness of the proposed framework has been compared with several famous state-of-the-art intelligent fault diagnosis methods existing in the literature. The comparative performance has been validated on benchmarked datasets, i.e., air compressor and bearing datasets collected from a single-stage reciprocating air compressor. The proposed framework is able to achieve the classification accuracy of 99.26% and 98.55% for bearing and air compressor datasets, respectively, with only ten samples per class. Moreover, a real-time case study is performed to validate the proposed method in real time.

A Multi-Schematic Classifier-Independent Oversampling Approach for Imbalanced Datasets

Labelled imbalanced data, used for classification problems, have an unequal distribution of samples over the classes. Traditional classification models, such as random forest, gradient boosting, face a problem when dealing with imbalanced datasets. Over 85 oversampling algorithms, mostly extensions of the SMOTE algorithm, have been built over the past two decades, to solve the problem of imbalanced datasets. However, it has been evident from previous studies that different oversampling algorithms have different degrees of efficiency with different classifiers. With numerous algorithms available, it is difficult to decide on an oversampling algorithm for a chosen classifier. Here, we overcome this problem with a multi-schematic and classifier-independent oversampling approach, referred to as ProWRAS (Proximity Weighted Random Affine Shadowsampling). ProWRAS integrates the Localized Random Affine Shadowsampling (LoRAS) algorithm and the Proximity Weighted Synthetic oversampling (ProWSyn) algorithm. By controlling the variance of the synthetic samples, as well as a proximity-weighted clustering system of the minority class data, the ProWRAS algorithm improves performance, compared to algorithms that generate synthetic samples through modelling high dimensional convex spaces of the minority class. ProWRAS is multi-schematic by employing four oversampling schemes, each of which has its unique way to model the variance of the generated data. The proximity weighted clustering approach of ProWRAS allows one to generate low variance synthetic samples only in borderline clusters to avoid overlap with the majority class. Most importantly, the performance of ProWRAS with proper choice of oversampling schemes, is independent of the classifier used. We have benchmarked our newly developed ProWRAS algorithm against five state-of-the-art oversampling models and four different classifiers on 20 publicly available datasets. Our results show that ProWRAS outperforms other oversampling algorithms in a statistically significant way, in terms of both F1-score and $\kappa $ -score. Moreover, we have introduced a novel measure for classifier independence $\mathcal {J}$ -score, and showed quantitatively that ProWRAS performs better, independent of the classifier used. Thus, ProWRAS is highly effective for homogeneous tabular data where convex modelling of the data space can be done. In practice, ProWRAS customizes synthetic sample generation according to a classifier of choice and thereby reduces benchmarking efforts.

Synthetic Sample Generation for Label Distribution Learning

Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than a single label or multiple labels. Current LDL methods have proven their effectiveness in many machine learning applications. As of the first formulation of the LDL problem, numerous studies have been carried out that apply the LDL methodology to various real-life problem solving. Others have focused more specifically on the proposal of new algorithms. The purpose of this article is to start addressing the LDL problem as of the data pre-processing stage. The baseline hypothesis is that, due to the high dimensionality of existing LDL data sets, it is very likely that this data will be incomplete and/or that poor data quality will lead to poor performance once applied to the learning algorithms. In this paper, we propose an oversampling method, which creates a superset of the original dataset by creating new instances from existing ones. Then, we apply already existing algorithms to the pre-processed training set in order to validate the effcacy of our method. The effectiveness of the proposed SSG-LDL is verified on several LDL datasets, showing significant improvements to the state-of-the-art LDL methods.

Cluster Quality based Non-Reductional (CQNR) oversampling technique and effector protein predictor based on 3D structure (EPP3D) of proteins

BackgroundEffector proteins of bacteria infect their hosts by specific dedicated machinery identified as secretion systems. Currently, no mechanism to identify the effector proteins based on their 3D structure has been reported in the literature. In order to identify effector proteins, extraction of features from their 3D structure is crucial. However, effector protein datasets are highly imbalanced. State-of-the-art oversampling algorithms are incapable of dealing with such datasets. They usually eliminate samples as noise. They do not ensure generation of synthetic samples strictly in the vicinity of the minority class samples. In effector protein datasets, deletion of any samples as noise would lead to loss of crucial information. Furthermore, generation of synthetic samples of the minority class in the vicinity of majority class samples would lead to an inept classifier. MethodIn this paper, we introduce an algorithm called Cluster Quality based Non-Reductional (CQNR) oversampling technique. Its novelty lies in generating new samples proportional to the distribution of samples of the minority classes, without eliminating any sample as noise. Utilizing CQNR, we develop a novel Effector Protein Predictor based on the 3D (EPP3D) structure of proteins. EPP3D is trained on a feature set, balanced by CQNR, comprising 3D structure-based features, namely, convex hull layer count, surface atom composition, radius of gyration, packing density and compactness, derived from the 3D structure of the experimentally verified effector proteins. ResultFscore and Gmean demonstrate that CQNR has outperformed some well-established oversampling methods by approximately 3–5%, with respect to classification accuracy, on five benchmark datasets and three other highly imbalanced synthetically generated datasets. Likewise, for classification of pathogenic effector proteins, a significant improvement of 7–9% in accuracy has been noticed, on the application of CQNR followed by EPP3D. Moreover, EPP3D has exhibited an improvement of 2–4% in classifying effector proteins based on their 3D structure compared to the classification of effector proteins based on their amino acid sequences. The software for CQNR and EPP3D are available at http://projectphd.droppages.com/CQNR.html.

Synthetic Sample Extension in Implementation of Tangut Character Databases

The Tangut script was a logographic writing system used for the extinct Tangut language of the Western Xia Dynasty, which spanned 1038 to 1227. The technic of optical character recognition, machine learning, and computer vision will help greatly in the unscrambling of the character in the ancient scripts. But all these technics are based on the character database, which provides learning samples and test standards. In the process of building the Tangut Character Databases using the ancient Tangut scripts as a data source, it is found that the problem of imbalanced class distribution significantly compromises the performance of learning algorithms. A method of synthetic sample generation was proposed in this paper to improve the performance of learning and recognition of Tangut characters. The comparison of recognition accuracy between the learning base in the original data set and the synthetic generated data set was demonstrated, and presented an impressive superiority utilizing the researchers’ method. The organization of Tangut character databases was also introduced in this paper.