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

Adaptive Online Sequential ELM for Concept Drift Tackling

A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect underfitting condition.Comment: Hindawi Publishing. Computational Intelligence and Neuroscience Volume 2016 (2016), Article ID 8091267, 17 pages Received 29 January 2016, Accepted 17 May 2016. Special Issue on "Advances in Neural Networks and Hybrid-Metaheuristics: Theory, Algorithms, and Novel Engineering Applications". Academic Editor: Stefan Hauf

A systematic review of data quality issues in knowledge discovery tasks

Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust

A New Large Scale SVM for Classification of Imbalanced Evolving Streams

Classification from imbalanced evolving streams possesses a combined challenge of class imbalance and concept drift (CI-CD). However, the state of imbalance is dynamic, a kind of virtual concept drift. The imbalanced distributions and concept drift hinder the online learner’s performance as a combined or individual problem. A weighted hybrid online oversampling approach,”weighted online oversampling large scale support vector machine (WOOLASVM),” is proposed in this work to address this combined problem. The WOOLASVM is an SVM active learning approach with new boundary weighing strategies such as (i) dynamically oversampling the current boundary and (ii) dynamic weighing of the cost parameter of the SVM objective function. Thus at any time step, WOOLASVM maintains balanced class distributions so that the CI-CD problem does not hinder the online learner performance. Over extensive experiments on synthetic and real-world streams with the static and dynamic state of imbalance, the WOOLASVM exhibits better online classification performances than other state-of-the-art methods

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Computational intelligence contributions to readmisision risk prediction in Healthcare systems

136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures

Probabilistic XGBoost Threshold Classification with Autoencoder for Credit Card Fraud Detection

Due to the imbalanced data of outnumbered legitimate transactions than the fraudulent transaction, the detection of fraud is a challenging task to find an effective solution. In this study, autoencoder with probabilistic threshold shifting of XGBoost (AE-XGB) for credit card fraud detection is designed. Initially, AE-XGB employs autoencoder the prevalent dimensionality reduction technique to extract data features from latent space representation. Then the reconstructed lower dimensional features utilize eXtreame Gradient Boost (XGBoost), an ensemble boosting algorithm with probabilistic threshold to classify the data as fraudulent or legitimate. In addition to AE-XGB, other existing ensemble algorithms such as Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), Random Forest, Categorical Boosting (CatBoost), LightGBM and XGBoost are compared with optimal and default threshold. To validate the methodology, we used IEEE-CIS fraud detection dataset for our experiment. Class imbalance and high dimensionality characteristics of dataset reduce the performance of model hence the data is preprocessed and trained. To evaluate the performance of the model, evaluation indicators such as precision, recall, f1-score, g-mean and Mathews Correlation Coefficient (MCC) are accomplished. The findings revealed that the performance of the proposed AE-XGB model is effective in handling imbalanced data and able to detect fraudulent transactions with 90.4% of recall and 90.5% of f1-score from incoming new transactions