Practical approaches to mining of clinical datasets : from frameworks to novel feature selection

Research has investigated clinical data that have embedded within them numerous complexities and uncertainties in the form of missing values, class imbalances and high dimensionality. The research in this thesis was motivated by these challenges to minimise these problems whilst, at the same time, maximising classification performance of data and also selecting the significant subset of variables. As such, this led to the proposal of a data mining framework and feature selection method. The proposed framework has a simple algorithmic framework and makes use of a modified form of existing frameworks to address a variety of different data issues, called the Handling Clinical Data Framework (HCDF). The assessment of data mining techniques reveals that missing values imputation and resampling data for class balancing can improve the performance of classification. Next, the proposed feature selection method was introduced; it involves projecting onto principal component method (FS-PPC) and draws on ideas from both feature extraction and feature selection to select a significant subset of features from the data. This method selects features that have high correlation with the principal component by applying symmetrical uncertainty (SU). However, irrelevant and redundant features are removed by using mutual information (MI). However, this method provides confidence in the selected subset of features that will yield realistic results with less time and effort. FS-PPC is able to retain classification performance and meaningful features while consisting of non-redundant features. The proposed methods have been practically applied to analysis of real clinical data and their effectiveness has been assessed. The results show that the proposed methods are enable to minimise the clinical data problems whilst, at the same time, maximising classification performance of data

PWIDB: A framework for learning to classify imbalanced data streams with incremental data re-balancing technique

The performance of classification algorithms with highly imbalanced streaming data depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied using static batch data to resolve the class imbalance problem, which is difficult if applied for massive data streams. In this paper, a new Piece-Wise Incremental Data re-Balancing (PWIDB) framework is proposed. The PWIDB framework combines automated balancing techniques using Racing Algorithm (RA) and incremental rebalancing technique. RA is an active learning approach capable of classifying imbalanced data and can provide a way to select an appropriate re-balancing technique with imbalanced data. In this paper, we have extended the capability of RA for handling imbalanced data streams in the proposed PWIDB framework. The PWIDB accumulates previous knowledge with increments of re-balanced data and captures the concept of the imbalanced instances. The PWIDB is an incremental streaming batch framework, which is suitable for learning with streaming imbalanced data. We compared the performance of PWIDB with a well-known FLORA technique. Experimental results show that the PWIDB framework exhibits an improved and stable performance compared to FLORA and accumulative re-balancing techniques

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

Handling Concept Drift in the Context of Expensive Labels

Machine learning has been successfully applied to a wide range of prediction problems, yet its application to data streams can be complicated by concept drift. Existing approaches to handling concept drift are overwhelmingly reliant on the assumption that it is possible to obtain the true label of an instance shortly after classification at a negligible cost. The aim of this thesis is to examine, and attempt to address, some of the problems related to handling concept drift when the cost of obtaining labels is high. This thesis presents Decision Value Sampling (DVS), a novel concept drift handling approach which periodically chooses a small number of the most useful instances to label. The newly labelled instances are then used to re-train the classifier, an SVM with a linear kernel, to handle any change in concept that might occur. In this way, only the instances that are required to keep the classifier up-to-date are labelled. The evaluation of the system indicates that a classifier can be kept up-to-date with changes in concept while only requiring 15% of the data stream to be labelled. In a data stream with a high throughput this represents a significant reduction in the number of labels required. The second novel concept drift handling approach proposed in this thesis is Confidence Distribution Batch Detection (CDBD). CDBD uses a heuristic based on the distribution of an SVM’s confidence in its predictions to decide when to rebuild the clas- sifier. The evaluation shows that CDBD can be used to reliably detect when a change in concept has taken place and that concept drift can be handled if the classifier is rebuilt when CDBD sig- nals a change in concept. The evaluation also shows that CDBD obtains a considerable labels saving as it only requires labelled data when a change in concept has been detected. The two concept drift handling approaches deal with concept drift in a different manner, DVS continuously adapts the clas- sifier, whereas CDBD only adapts the classifier when a sizeable change in concept is suspected. They reflect a divide also found in the literature, between continuous rebuild approaches (like DVS) and triggered rebuild approaches (like CDBD). The final major contribution in this thesis is a comparison between continuous and triggered rebuild approaches, as this is an underexplored area. An empirical comparison between representative techniques from both types of approaches shows that triggered rebuild works slightly better on large datasets where the changes in concepts occur infrequently, but in general a continuous rebuild approach works the best