Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications

Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties create bias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a well-known data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

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

Fast Machine Learning Algorithms for Massive Datasets with Applications in the Biomedical Domain

The continuous increase in the size of datasets introduces computational challenges for machine learning algorithms. In this dissertation, we cover the machine learning algorithms and applications in large-scale data analysis in manufacturing and healthcare. We begin with introducing a multilevel framework to scale the support vector machine (SVM), a popular supervised learning algorithm with a few tunable hyperparameters and highly accurate prediction. The computational complexity of nonlinear SVM is prohibitive on large-scale datasets compared to the linear SVM, which is more scalable for massive datasets. The nonlinear SVM has shown to produce significantly higher classification quality on complex and highly imbalanced datasets. However, a higher classification quality requires a computationally expensive quadratic programming solver and extra kernel parameters for model selection. We introduce a generalized fast multilevel framework for regular, weighted, and instance weighted SVM that achieves similar or better classification quality compared to the state-of-the-art SVM libraries such as LIBSVM. Our framework improves the runtime more than two orders of magnitude for some of the well-known benchmark datasets. We cover multiple versions of our proposed framework and its implementation in detail. The framework is implemented using PETSc library which allows easy integration with scientific computing tasks. Next, we propose an adaptive multilevel learning framework for SVM to reduce the variance between prediction qualities across the levels, improve the overall prediction accuracy, and boost the runtime. We implement multi-threaded support to speed up the parameter fitting runtime that results in more than an order of magnitude speed-up. We design an early stopping criteria to reduce the extra computational cost when we achieve expected prediction quality. This approach provides significant speed-up, especially for massive datasets. Finally, we propose an efficient low dimensional feature extraction over massive knowledge networks. Knowledge networks are becoming more popular in the biomedical domain for knowledge representation. Each layer in knowledge networks can store the information from one or multiple sources of data. The relationships between concepts or between layers represent valuable information. The proposed feature engineering approach provides an efficient and highly accurate prediction of the relationship between biomedical concepts on massive datasets. Our proposed approach utilizes semantics and probabilities to reduce the potential search space for the exploration and learning of machine learning algorithms. The calculation of probabilities is highly scalable with the size of the knowledge network. The number of features is fixed and equivalent to the number of relationships or classes in the data. A comprehensive comparison of well-known classifiers such as random forest, SVM, and deep learning over various features extracted from the same dataset, provides an overview for performance and computational trade-offs. Our source code, documentation and parameters will be available at https://github.com/esadr/