Population structure-learned classifier for high-dimension low-sample-size class-imbalanced problem

The Classification on high-dimension low-sample-size data (HDLSS) is a challenging problem and it is common to have class-imbalanced data in most application fields. We term this as Imbalanced HDLSS (IHDLSS). Recent theoretical results reveal that the classification criterion and tolerance similarity are crucial to HDLSS, which emphasizes the maximization of within-class variance on the premise of class separability. Based on this idea, a novel linear binary classifier, termed Population Structure-learned Classifier (PSC), is proposed. The proposed PSC can obtain better generalization performance on IHDLSS by maximizing the sum of inter-class scatter matrix and intra-class scatter matrix on the premise of class separability and assigning different intercept values to majority and minority classes. The salient features of the proposed approach are: (1) It works well on IHDLSS; (2) The inverse of high dimensional matrix can be solved in low dimensional space; (3) It is self-adaptive in determining the intercept term for each class; (4) It has the same computational complexity as the SVM. A series of evaluations are conducted on one simulated data set and eight real-world benchmark data sets on IHDLSS on gene analysis. Experimental results demonstrate that the PSC is superior to the state-of-art methods in IHDLSS.Comment: 41 pages,10 Figures,10 Table

Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values

This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.Comment: arXiv admin note: substantial text overlap with arXiv:1503.0625

PERFORMANCE ANALYSIS OF THE IMBALANCED DATA METHOD ON INCREASING THE CLASSIFICATION ACCURACY OF THE MACHINE LEARNING HYBRID METHOD

This study analyzes the performance of hybrid methods in improving accuracy on imbalanced data using Dengue Hemorrhagic Fever Case Data from 2017 to 2021 in Bandung City. The attributes used in this study consist of Total Population, Total Male, Elementary School Graduation, Junior High School Graduation, High School Graduation, College Graduation, Rainfall, Average Temperature, Humidity, Male Cases, Number of Cases, and Class. This research combines five Machine Learning methods, such as Decision Tree, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor, and Nave Bayes. Hybrid Methods used in this research are Voting and Stacking methods. The oversampling methods used to handle imbalanced data in this study are Random Oversampling and Adasyn. The results show that Voting and Stacking without Random Oversampling and Adasyn get the same accuracy of 88,88%. While using Random Oversampling, voting gets an accuracy of 95,37% and stacking gets an accuracy of 96,29%. While using Adasyn, voting gets an accuracy of 94,44% and stacking gets an accuracy of 97,22%. Based on the results obtained, it can be concluded that the Random Oversampling and Adasyn Method can improve the performance of the Machine Learning hybrid method on imbalanced data. The contribution of this research is to provide information on the study and analysis of the implementation of the Random Oversampling and Adasyn methods in improving the performance of the Voting and Stacking methods in hybrid classification

The classification for High-dimension low-sample size data

Huge amount of applications in various fields, such as gene expression analysis or computer vision, undergo data sets with high-dimensional low-sample-size (HDLSS), which has putted forward great challenges for standard statistical and modern machine learning methods. In this paper, we propose a novel classification criterion on HDLSS, tolerance similarity, which emphasizes the maximization of within-class variance on the premise of class separability. According to this criterion, a novel linear binary classifier is designed, denoted by No-separated Data Maximum Dispersion classifier (NPDMD). The objective of NPDMD is to find a projecting direction w in which all of training samples scatter in as large an interval as possible. NPDMD has several characteristics compared to the state-of-the-art classification methods. First, it works well on HDLSS. Second, it combines the sample statistical information and local structural information (supporting vectors) into the objective function to find the solution of projecting direction in the whole feature spaces. Third, it solves the inverse of high dimensional matrix in low dimensional space. Fourth, it is relatively simple to be implemented based on Quadratic Programming. Fifth, it is robust to the model specification for various real applications. The theoretical properties of NPDMD are deduced. We conduct a series of evaluations on one simulated and six real-world benchmark data sets, including face classification and mRNA classification. NPDMD outperforms those widely used approaches in most cases, or at least obtains comparable results.Comment: arXiv admin note: text overlap with arXiv:1901.0137

A Selective Sampling Method for Imbalanced Data Learning on Support Vector Machines

The class imbalance problem in classification has been recognized as a significant research problem in recent years and a number of methods have been introduced to improve classification results. Rebalancing class distributions (such as over-sampling or under-sampling of learning datasets) has been popular due to its ease of implementation and relatively good performance. For the Support Vector Machine (SVM) classification algorithm, research efforts have focused on reducing the size of learning sets because of the algorithm\u27s sensitivity to the size of the dataset. In this dissertation, we propose a metaheuristic approach (Genetic Algorithm) for under-sampling of an imbalanced dataset in the context of a SVM classifier. The goal of this approach is to find an optimal learning set from imbalanced datasets without empirical studies that are normally required to find an optimal class distribution. Experimental results using real datasets indicate that this metaheuristic under-sampling performed well in rebalancing class distributions. Furthermore, an iterative sampling methodology was used to produce smaller learning sets by removing redundant instances. It incorporates informative and the representative under-sampling mechanisms to speed up the learning procedure for imbalanced data learning with a SVM. When compared with existing rebalancing methods and the metaheuristic approach to under-sampling, this iterative methodology not only provides good performance but also enables a SVM classifier to learn using very small learning sets for imbalanced data learning. For large-scale imbalanced datasets, this methodology provides an efficient and effective solution for imbalanced data learning with an SVM

Training host-pathogen protein–protein interaction predictors

Detection of protein–protein interactions (PPIs) plays a vital role in molecular biology. Particularly, pathogenic infections are caused by interactions of host and pathogen proteins. It is important to identify host–pathogen interactions (HPIs) to discover new drugs to counter infectious diseases. Conventional wet lab PPI detection techniques have limitations in terms of cost and large-scale application. Hence, computational approaches are developed to predict PPIs. This study aims to develop machine learning models to predict inter-species PPIs with a special interest in HPIs. Specifically, we focus on seeking answers to three questions that arise while developing an HPI predictor: (1) How should negative training examples be selected? (2) Does assigning sample weights to individual negative examples based on their similarity to positive examples improve generalization performance? and, (3) What should be the size of negative samples as compared to the positive samples during training and evaluation? We compare two available methods for negative sampling: random versus DeNovo sampling and our experiments show that DeNovo sampling offers better accuracy. However, our experiments also show that generalization performance can be improved further by using a soft DeNovo approach that assigns sample weights to negative examples inversely proportional to their similarity to known positive examples during training. Based on our findings, we have also developed an HPI predictor called HOPITOR (Host-Pathogen Interaction Predictor) that can predict interactions between human and viral proteins. The HOPITOR web server can be accessed at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor