Misclassification analysis for the class imbalance problem

In classification, the class imbalance issue normally causes the learning algorithm to be dominated by the majority classes and the features of the minority classes are sometimes ignored. This will indirectly affect how human visualise the data. Therefore, special care is needed to take care of the learning algorithm in order to enhance the accuracy for the minority classes. In this study, the use of misclassification analysis is investigated for data re-distribution. Several under-sampling techniques and hybrid techniques using misclassification analysis are proposed in the paper. The benchmark data sets obtained from the University of California Irvine (UCI) machine learning repository are used to investigate the performance of the proposed techniques. The results show that the proposed hybrid technique presents the best performance in the experiment

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Equilibrating the recognition of the minority Class in the imbalance context

In pattern recognition, it is well known that the classifier performance depends on the classification rule and the complexities presented in the data sets (such as class overlapping, class imbalance, outliers, high-dimensional data sets among others). In this way, the issue of class imbalance is exhibited when one class is less represented with respect to the other classes. If the classifier is trained with imbalanced data sets, the natural tendency is to recognize the samples included in the majority class, ignoring the minority classes. This situation is not desirable because in real problems it is necessary to recognize the minority class more without sacrificing the precision of the majority class. In this work we analyze the behaviour of four classifiers taking into a count a relative balance among the accuracy classes

Fundamental remote sensing science research program. Part 1: Status report of the mathematical pattern recognition and image analysis project

The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of the Earth from remotely sensed measurement of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inference about the Earth

DPWeka: Achieving Differential Privacy in WEKA

Organizations belonging to the government, commercial, and non-profit industries collect and store large amounts of sensitive data, which include medical, financial, and personal information. They use data mining methods to formulate business strategies that yield high long-term and short-term financial benefits. While analyzing such data, the private information of the individuals present in the data must be protected for moral and legal reasons. Current practices such as redacting sensitive attributes, releasing only the aggregate values, and query auditing do not provide sufficient protection against an adversary armed with auxiliary information. In the presence of additional background information, the privacy protection framework, differential privacy, provides mathematical guarantees against adversarial attacks. Existing platforms for differential privacy employ specific mechanisms for limited applications of data mining. Additionally, widely used data mining tools do not contain differentially private data mining algorithms. As a result, for analyzing sensitive data, the cognizance of differentially private methods is currently limited outside the research community. This thesis examines various mechanisms to realize differential privacy in practice and investigates methods to integrate them with a popular machine learning toolkit, WEKA. We present DPWeka, a package that provides differential privacy capabilities to WEKA, for practical data mining. DPWeka includes a suite of differential privacy preserving algorithms which support a variety of data mining tasks including attribute selection and regression analysis. It has provisions for users to control privacy and model parameters, such as privacy mechanism, privacy budget, and other algorithm specific variables. We evaluate private algorithms on real-world datasets, such as genetic data and census data, to demonstrate the practical applicability of DPWeka