Oversampling Algorithms Research Articles

MLAWSMOTE: Oversampling in Imbalanced Multi-label Classification with Missing Labels by Learning Label Correlation Matrix

Missing labels in multi-label datasets are a common problem, especially for minority classes, which are more likely to occur. This limitation hinders the performance of classifiers in identifying and extracting information from minority classes. Oversampling is an effective method for addressing imbalanced multi-label problems by generating synthetic instances to create a class-balanced dataset. However, the existing oversampling algorithms mainly focus on the location of the generated data, and there is a lack of design on how to complete the labels of the synthetic data. To address this issue, we propose MLAWSMOTE, a synthetic data generation algorithm based on matrix factorization weights. We introduce a weak supervised learning method in the oversampling method, optimize the weights of features and labels by using label correlation, and iteratively learn the ideal label weights. The mapping relationship between features and labels is learned from the dataset and the label correlation matrix. The oversampling ratio is defined based on the discrepancy between observed labels and the ideal label of synthetic instances. It mitigates the impact of missing minority labels on the model’s predictions. The labeling of synthetic instances is performed based on label prediction, and the potential labeling distribution is complemented. Experimental results on multiple multi-label datasets under different label missing ratios demonstrate the effectiveness of the proposed method in terms of ACC, Hamming loss, MacroF1 and MicroF1. In the validation of the four classifiers, MacroF1 decreased by 24.78%, 17.81%, 3.8% and 19.56%, respectively, with the increase of label loss rate. After applying MLAWSMOTE only decreased by 15.79%, 13.63%, 3.78% and 15.21%.

Natural local density-based adaptive oversampling algorithm for imbalanced classification

Oversampling-based methods achieve impressive performance for classifying imbalanced data. However, many existing oversampling algorithms are still very sensitive to noise. Besides, these algorithms also require one or more parameters, how to set these parameters is very challenging. Furthermore, the generated samples by these algorithms are meaningless or unsafe. To solve these problems, a novel oversampling algorithm for unbalanced classification is presented, named Natural Local Density-based Adaptive Oversampling algorithm (NLDAO). NLDAO has four main advantages: (a) it does not need any parameter due to the use of natural neighbors to calculate local density; (b) it applies a noisy filter to remove noises, which makes the sample boundary cleaner so that the proposed method is robust to noise; (c) it unevenly distributes the generated samples to the minority class, which maintains the original characteristics and enhances the boundary classification ability; (d) it determines the generated region by tracking the sample proportion drop that declines the randomness of the synthesized and reduces the noisy generation. Finally, in experiments, we apply the NLDAO algorithm to artificial datasets to visually demonstrate its effectiveness. Moreover, intensive experiments on real datasets show that NLDAO can achieve better performance than state-of-the-art methods.

A Synthetic Minority Oversampling Technique Based on Gaussian Mixture Model Filtering for Imbalanced Data Classification.

Data imbalance is a common phenomenon in machine learning. In the imbalanced data classification, minority samples are far less than majority samples, which makes it difficult for minority to be effectively learned by classifiers. A synthetic minority oversampling technique (SMOTE) improves the sensitivity of classifiers to minority by synthesizing minority samples without repetition. However, the process of synthesizing new samples in the SMOTE algorithm may lead to problems such as "noisy samples" and "boundary samples." Based on the above description, we propose a synthetic minority oversampling technique based on Gaussian mixture model filtering (GMF-SMOTE). GMF-SMOTE uses the expected maximum algorithm based on the Gaussian mixture model to group the imbalanced data. Then, the expected maximum filtering algorithm is used to filter out the "noisy samples" and "boundary samples" in the subclasses after grouping. Finally, to synthesize majority and minority samples, we design two dynamic oversampling ratios. Experimental results show that the GMF-SMOTE performs better than the traditional oversampling algorithms on 20 UCI datasets. The population averages of sensitivity and specificity indexes of random forest (RF) on the UCI datasets synthesized by GMF-SMOTE are 97.49% and 97.02%, respectively. In addition, we also record the G-mean and MCC indexes of the RF, which are 97.32% and 94.80%, respectively, significantly better than the traditional oversampling algorithms. More importantly, the two statistical tests show that GMF-SMOTE is significantly better than the traditional oversampling algorithms.

Improved Oversampling Algorithm for Imbalanced Data Based on K-Nearest Neighbor and Interpolation Process Optimization

The problems of imbalanced datasets are generally considered asymmetric issues. In asymmetric problems, artificial intelligence models may exhibit different biases or preferences when dealing with different classes. In the process of addressing class imbalance learning problems, the classification model will pay too much attention to the majority class samples and cannot guarantee the classification performance of the minority class samples, which might be more valuable. By synthesizing the minority class samples and changing the data distribution, unbalanced datasets can be optimized. Traditional oversampling algorithms have problems of blindness and boundary ambiguity when synthesizing new samples. A modified reclassification algorithm based on Gaussian distribution is put forward. First, the minority class samples are reclassified by the KNN algorithm. Then, different synthesis strategies are selected according to the combination of the minority class samples, and the Gaussian distribution is used to replace the uniform random distribution for interpolation operation under certain classification conditions to reduce the possibility of generating noise samples. The experimental results indicate that the proposed oversampling algorithm can achieve a performance improvement of 2∼8% in evaluation metrics, including G-mean, F-measure, and AUC, compared to traditional oversampling algorithms.

An overlapping minimization-based over-sampling algorithm for binary imbalanced classification

Imbalanced learning is an important branch of machine learning. It addresses the challenge of improving classifier accuracy for minority classes in imbalanced data sets. Currently, the mainstream methods for handling imbalanced learning are the synthetic minority oversampling technique (SMOTE) and its variants, which generate synthetic minority class samples to balance the dataset. However, existing methods suffer from issues such as increased sample overlap, exacerbated intra-class imbalance, and are sensitive to parameter settings. These issues make it challenging to generate high-quality minority class samples and can adversely affect the dataset. To address these challenges, this study proposes a novel overlapping minimization-based over-sampling (OMOS) algorithm for binary imbalanced classification. The OMOS algorithm consists of four steps: clustering, filtering, auto-encoding, and oversampling. In the clustering step, the mean shift algorithm is utilized to cluster the original dataset and identify clusters that belong to the minority class. In the filtering step, safe samples are selected that maintain consistent labels before and after clustering. Then, in the auto-encoding step, autoencoders are utilized to capture the distribution characteristics of safe samples within each minority class cluster. Finally, in the last step, minority class samples are generated based on the probability distribution learned from safe samples. Furthermore, OMOS introduces a novel approach to compute suitable sampling rates for each minority class cluster to handle intra-class imbalance. Experimental results show that the proposed OMOS algorithm outperforms six state-of-the-art SMOTE-based oversampling algorithms on 20 real-world imbalanced datasets and four classifiers: naive Bayes classifier, support vector machine, logistic regression, and decision trees. This demonstrates that OMOS is effective for binary imbalanced classification tasks.

Attention features selection oversampling technique (AFS-O) for rolling bearing fault diagnosis with class imbalance

When using data-driven methods for fault diagnosis of mechanical rotating components such as gears and bearings, there is a problem of class imbalance in the lifecycle data collected by sensors. The most commonly used method to address this issue is the synthetic minority over-sampling technique, which synthesizes samples in the feature space, but its blind synthesis may lead to redundant features in the synthetic samples. To avoid this deficiency, this paper proposes a feature-weighted oversampling method called AFS-O (Attention Features Selection Oversampling Technique). First, time–frequency domain features are extracted from the full lifecycle data of bearings to construct an initial subset of features, which serves the input to AFS. Then, AFS is then used to obtain the distribution of feature selection patterns and generate feature weights to determine the inclusion or exclusion of each feature, thereby constructing an optimal subset of features. Finally, the optimal feature subset is synthetically oversampled to achieve class-balanced data, which is then fed into a classifier. AFS-O is applied to the rolling bearing accelerated lifetime dataset from Xi’an Jiaotong University. Experimental results demonstrate that AFS-O outperforms other state-of-the-art synthetic oversampling algorithms in terms of Gmean, , and Recall, confirming the effectiveness of the proposed method.

COMPARISON OF DATASET OVERSAMPLING ALGORITHMS AND THEIR APPLICABILITY TO THE CATEGORIZATION PROBLEM

The subject of research in the article is the problem of classification in machine learning in the presence of imbalanced classes in datasets. The purpose of the work is to analyze existing solutions and algorithms for solving the problem of dataset imbalance of different types and different industries and to conduct an experimental comparison of algorithms. The article solves the following tasks: to analyze approaches to solving the problem – preprocessing methods, learning methods, hybrid methods and algorithmic approaches; to define and describe the oversampling algorithms most often used to balance datasets; to select classification algorithms that will serve as a tool for establishing the quality of balancing by checking the applicability of the datasets obtained after oversampling; to determine metrics for assessing the quality of classification for comparison; to conduct experiments according to the proposed methodology. For clarity, we considered datasets with varying degrees of imbalance (the number of instances of the minority class was equal to 15, 30, 45, and 60% of the number of samples of the majority class). The following methods are used: analytical and inductive methods for determining the necessary set of experiments and building hypotheses regarding their results, experimental and graphic methods for obtaining a visual comparative characteristic of the selected algorithms. The following results were obtained: with the help of quality metrics, an experiment was conducted for all algorithms on two different datasets – the Titanic passenger dataset and the dataset for detecting fraudulent transactions in bank accounts. The obtained results indicated the best applicability of SMOTE and SVM SMOTE algorithms, the worst performance of Borderline SMOTE and k-means SMOTE, and at the same time described the results of each algorithm and the potential of their usage. Conclusions: the application of the analytical and experimental method provided a comprehensive comparative description of the existing balancing algorithms. The superiority of oversampling algorithms over undersampling algorithms was proven. The selected algorithms were compared using different classification algorithms. The results were presented using graphs and tables, as well as demonstrated in general using heat maps. Conclusions that were made can be used when choosing the optimal balancing algorithm in the field of machine learning.

Irregular characteristic analysis of 3D particles—A novel virtual sieving technique

For digitizing the granular material systems, including asphalt mixture, the sieving size of the particles plays an irreplaceable role. However, the traditional methods based on equivalent conversion performed terribly in measuring the size of individual particles. This study combined digital image processing, ensemble learning, and over-sampling algorithms to develop a novel sieving method. Besides, the comprehensive importance ranking of variables was put forward to assist in optimizing the random forest. The results show that the data collection method proposed in this study can quickly gain bulks of digital aggregates without a complicated reconstruction process. The synthetic minority over-sampling algorithm was conducive to the accuracy ascension by balancing the database. By scoring variables' relative importance and function, the variable selection method can effectively reduce system complexity, thus improving computational efficiency and robustness. Based on the above-improving processes, the measuring precision of the aggregates' sieving opening was raised by over 20%.

A Novel Approach to Decision-Making on Diagnosing Oncological Diseases Using Machine Learning Classifiers Based on Datasets Combining Known and/or New Generated Features of a Different Nature

This paper deals with the problem of diagnosing oncological diseases based on blood protein markers. The goal of the study is to develop a novel approach in decision-making on diagnosing oncological diseases based on blood protein markers by generating datasets that include various combinations of features: both known features corresponding to blood protein markers and new features generated with the help of mathematical tools, particularly with the involvement of the non-linear dimensionality reduction algorithm UMAP, formulas for various entropies and fractal dimensions. These datasets were used to develop a group of multiclass kNN and SVM classifiers using oversampling algorithms to solve the problem of class imbalance in the dataset, which is typical for medical diagnostics problems. The results of the experimental studies confirmed the feasibility of using the UMAP algorithm and approximation entropy, as well as Katz and Higuchi fractal dimensions to generate new features based on blood protein markers. Various combinations of these features can be used to expand the set of features from the original dataset in order to improve the quality of the received classification solutions for diagnosing oncological diseases. The best kNN and SVM classifiers were developed based on the original dataset augmented respectively with a feature based on the approximation entropy and features based on the UMAP algorithm and the approximation entropy. At the same time, the average values of the metric MacroF1-score used to assess the quality of classifiers during cross-validation increased by 16.138% and 4.219%, respectively, compared to the average values of this metric in the case when the original dataset was used in the development of classifiers of the same name.

Subspace-based minority oversampling for imbalance classification

In pattern classification, the class imbalance problem always occurs when the number of observations in some classes is significantly different from that of other categories, which leads to the learning bias in the classifiers. One possible solution to this problem is to re-balance the training set by over-sampling the minority class. However, over-samplings always push the classification boundaries to the majority part, thus the recall increases while the precision decreases. To avoid this situation and better handle the class imbalance problem, this paper proposes a new over-sampling method, namely Subspace-based Minority Over-Sampling (abbr. SMO). This approach considers that each category of samples is formed by common and unique characteristics, and such characteristics can be extracted by subspace. To obtain the balanced data, the common part is over-sampled for more accurately depicting the minority, and the unique part can be expanded by some generative methods. The balanced data are obtained by restoring the generated products of the subspace to the original space. The experimental results demonstrate that the SMO has the ability to model complex data distributions and outperforms both classical and newly designed over-sampling algorithms. Also, SMO can be used to generate simple images, and the generation results of MNIST can be clearly identified by both human vision and machine vision.

Feature-Ensemble-Based Crop Mapping for Multi-Temporal Sentinel-2 Data Using Oversampling Algorithms and Gray Wolf Optimizer Support Vector Machine

Accurate spatial distribution and area of crops are important basic data for assessing agricultural productivity and ensuring food security. Traditional classification methods tend to fit most categories, which will cause the classification accuracy of major crops and minor crops to be too low. Therefore, we proposed an improved Gray Wolf Optimizer support vector machine (GWO-SVM) method with oversampling algorithm to solve the imbalance-class problem in the classification process and improve the classification accuracy of complex crops. Fifteen feature bands were selected based on feature importance evaluation and correlation analysis. Five different smote methods were used to detect samples imbalanced with respect to major and minor crops. In addition, the classification results were compared with support vector machine (SVM) and random forest (RF) classifier. In order to improve the classification accuracy, we proposed a combined improved GWO-SVM algorithm, using an oversampling algorithm(smote) to extract major crops and minor crops and use SVM and RF as classification comparison methods. The experimental results showed that band 2 (B2), band 4 (B4), band 6 (B6), band 11 (B11), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) had higher feature importance. The classification results oversampling- based of smote, smote-enn, borderline-smote1, borderline-smote2, and distance-smote were significantly improved, with accuracy 2.84%, 2.66%, 3.94%, 4.18%, 6.96% higher than that those without 26 oversampling, respectively. At the same time, compared with SVM and RF, the overall accuracy of improved GWO-SVM was improved by 0.8% and 1.1%, respectively. Therefore, the GWO-SVM model in this study not only effectively solves the problem of equilibrium of complex crop samples in the classification process, but also effectively improves the overall classification accuracy of crops in complex farming areas, thus providing a feasible alternative for large-scale and complex crop mapping.

METAbolomics data Balancing with Over-sampling Algorithms (META-BOA): an online resource for addressing class imbalance.

Class imbalance, or unequal sample sizes between classes, is an increasing concern in machine learning for metabolomic and lipidomic data mining, which can result in overfitting for the over-represented class. Numerous methods have been developed for handling class imbalance, but they are not readily accessible to users with limited computational experience. Moreover, there is no resource that enables users to easily evaluate the effect of different over-sampling algorithms. METAbolomics data Balancing with Over-sampling Algorithms (META-BOA) is a web-based application that enables users to select between four different methods for class balancing, followed by data visualization and classification of the sample to observe the augmentation effects. META-BOA outputs a newly balanced dataset, generating additional samples in the minority class, according to the user's choice of Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE (BSMOTE), Adaptive Synthetic (ADASYN) or Random Over-Sampling Examples (ROSE). To present the effect of over-sampling on the data META-BOA further displays both principal component analysis and t-distributed stochastic neighbor embedding visualization of data pre- and post-over-sampling. Random forest classification is utilized to compare sample classification in both the original and balanced datasets, enabling users to select the most appropriate method for their further analyses. META-BOA is available at https://complimet.ca/meta-boa. Supplementary data are available at Bioinformatics online.

SW: A weighted space division framework for imbalanced problems with label noise

Imbalanced data learning is a ubiquitous challenge in data mining and machine learning. In particular, the ubiquity and inevitability of noise can exacerbate severe performance degradation. The synthetic minority oversampling technique (SMOTE) and its variants have been proposed. The core ideas of these variants are emphasizing the specific area or combining it with different noise filters; they introduce additional parameters that are difficult to optimize or rely on specific noise filters. Furthermore, SMOTE-based methods randomly select the nearest neighbor samples and perform random interpolation to synthesize new samples without considering the impact of the sample space’s chaotic degree. In this study, a framework called SW is proposed, which performs weighted sampling by calculating the sample space’s chaos. It is a general, robust and adaptive framework that copes with noisy imbalanced datasets and combines various oversampling algorithms to improve their performances. In the SW framework, the complete random forest (CRF) is introduced to divide the sample space and adaptively assign weights to distinguish and filter noisy and outlier samples. When synthesizing a new sample, the SW framework selects the seed samples’ neighbors and calculates the informed position using the derived weights, bringing the new sample closer to the safe area. Experimental results on 16 benchmark datasets and eight classic classifiers with eight pairs of representative oversampling algorithms demonstrate the SW framework’s effectiveness. The SW framework improves significantly in high-noise situations. In particular, SW-kmeans-SMOTE improved by approximately 5 % on average across all the metrics. Code and framework are available at https://github.com/dream-lm/SW_framework.

Seabed Modelling by Means of Airborne Laser Bathymetry Data and Imbalanced Learning for Offshore Mapping.

An important problem associated with the aerial mapping of the seabed is the precise classification of point clouds characterizing the water surface, bottom, and bottom objects. This study aimed to improve the accuracy of classification by addressing the asymmetric amount of data representing these three groups. A total of 53 Synthetic Minority Oversampling Technique (SMOTE) algorithms were adjusted and evaluated to balance the amount of data. The prepared data set was used to train the Multi-Layer Perceptron (MLP) neural network used for classifying the point cloud. Data balancing contributed to significantly increasing the accuracy of classification. The best overall classification accuracy achieved varied from 95.8% to 97.0%, depending on the oversampling algorithm used, and was significantly better than the classification accuracy obtained for unbalanced data and data with downsampling (89.6% and 93.5%, respectively). Some of the algorithms allow for 10% increased detection of points on the objects compared to unbalanced data or data with simple downsampling. The results suggest that the use of selected oversampling algorithms can aid in improving the point cloud classification and making the airborne laser bathymetry technique more appropriate for seabed mapping.

SA-CGAN: An oversampling method based on single attribute guided conditional GAN for multi-class imbalanced learning

Imbalanced data can always be observed in our daily life and various practical tasks. A lot of well-constructed machine learning methodologies may produce ineffective performance, when conducted on this kind of data. This originates from the produced high training biases that towards the majority class instances. Among all the solutions of this problem, data generation of the minority class is always considered the most effective approach. However, in all the previous works, data are always processed sample-wisely and the distribution of each single data attribute is never noticed. So, in this paper, to estimate the mechanism of how each attribute contributes to its label, we explore the potential connection between the two items by Conditional Generative Adversarial Networks (CGAN) separately and individually. Then, the constructed new instances are purified by a designed attribute-based minimax filter and the survivors are concatenated to form the eventual generated data. In other words, different from the CGAN based data generation way, the proposed approach improves it by additionally considering all the single attribute patterns of the data that to construct new instances. In addition, we extend the binary class imbalanced learning framework to multiple class one. In the experimental part, the improved model is compared against GAN, CGAN and some other standard multiple-class oversampling algorithms on several widely used datasets. Results, in terms of four common measurements, have shown that the proposed approach can produce comparable and always superior performance when compared with the competitors.

Improved CBSO: A distributed fuzzy-based adaptive synthetic oversampling algorithm for imbalanced judicial data

Imbalanced data problem is a big challenge for judicial data analysis since it often leads to a low accuracy of the data classification. Synthesizing new samples by means of oversampling is a useful method to handle this problem. However, most oversampling algorithms have been obtained regardless of noise samples and the data distribution has not been fully taken into consideration. For this purpose, an improved cluster-based synthetic oversampling algorithm, namely distributed fuzzy-based adaptive synthetic oversampling (DFBASO) algorithm, is proposed by simultaneously considering the distribution of inter-class, the distribution of intra-cluster and the characteristic of noise samples. The proposed DFBASO algorithm is equipped with: 1) fuzzy c-means (FCM) clustering algorithm application for samples of minority and majority classes; 2) weighted distribution based on two factors including the inter-class distance and the cluster capacity; and 3) a mixed synthetic method under different distribution cases of intra-cluster. Finally, the judicial data set and eight public data sets are utilized to show the effectiveness and universal applicability of the proposed DFBASO algorithm for the imbalanced data classification.

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.

An Improving Majority Weighted Minority Oversampling Technique for Imbalanced Classification Problem

Minority oversampling techniques have played a pivotal role in the field of imbalanced learning. While traditional oversampling algorithms can cause problems such as intra-class imbalance of samples, ignoring important information of boundary samples, and high similarity between new and old samples. Based on the situation, we proposed a new type of over-sampling method, BIRCH and Boundary Midpoint Centroid Synthetic Minority Over-Sampling Technique (BI-BMCSMOTE). First of all, the algorithm used the BIRCH clustering method to achieve quick cluster of the minority samples. After identifying and removing the noise, it marked the boundary minority samples in the label by probability. Secondly, it generated a density function for each sample cluster, calculated its density and sampling weight, performed midpoint composite sampling among the minority samples marked by probability and other minority samples in each cluster, and then calculated and analyzed the specific value of composite sampling to improve the accuracy of the model. According to the experimental results, the algorithm was proved to be valid.

Integrating Second-order Moving Average and Over-sampling Algorithm to Predict Apoptosis Protein Subcellular Localization

Background: Apoptosis proteins have a key role in the development and the homeostasis of the organism, and are very important to understand the mechanism of cell proliferation and death. The function of apoptosis protein is closely related to its subcellular location. Objective: Prediction of apoptosis protein subcellular localization is a meaningful task. Methods: In this study, we predict the apoptosis protein subcellular location by using the PSSMbased second-order moving average descriptor, nonnegative matrix factorization based on Kullback-Leibler divergence and over-sampling algorithms. This model is named by SOMAPKLNMF- OS and constructed on the ZD98, ZW225 and CL317 benchmark datasets. Then, the support vector machine is adopted as the classifier, and the bias-free jackknife test method is used to evaluate the accuracy. Results: Our prediction system achieves the favorable and promising performance of the overall accuracy on the three datasets and also outperforms the other listed models. Conclusion: The results show that our model offers a high throughput tool for the identification of apoptosis protein subcellular localization.

Identification of Orphan Genes in Unbalanced Datasets Based on Ensemble Learning.

Orphan genes are associated with regulatory patterns, but experimental methods for identifying orphan genes are both time-consuming and expensive. Designing an accurate and robust classification model to detect orphan and non-orphan genes in unbalanced distribution datasets poses a particularly huge challenge. Synthetic minority over-sampling algorithms (SMOTE) are selected in a preliminary step to deal with unbalanced gene datasets. To identify orphan genes in balanced and unbalanced Arabidopsis thaliana gene datasets, SMOTE algorithms were then combined with traditional and advanced ensemble classified algorithms respectively, using Support Vector Machine, Random Forest (RF), AdaBoost (adaptive boosting), GBDT (gradient boosting decision tree), and XGBoost (extreme gradient boosting). After comparing the performance of these ensemble models, SMOTE algorithms with XGBoost achieved an F1 score of 0.94 with the balanced A. thaliana gene datasets, but a lower score with the unbalanced datasets. The proposed ensemble method combines different balanced data algorithms including Borderline SMOTE (BSMOTE), Adaptive Synthetic Sampling (ADSYN), SMOTE-Tomek, and SMOTE-ENN with the XGBoost model separately. The performances of the SMOTE-ENN-XGBoost model, which combined over-sampling and under-sampling algorithms with XGBoost, achieved higher predictive accuracy than the other balanced algorithms with XGBoost models. Thus, SMOTE-ENN-XGBoost provides a theoretical basis for developing evaluation criteria for identifying orphan genes in unbalanced and biological datasets.