1,106 research outputs found

    Model-based classification for subcellular localization prediction of proteins

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    Methods to Improve the Prediction Accuracy and Performance of Ensemble Models

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    The application of ensemble predictive models has been an important research area in predicting medical diagnostics, engineering diagnostics, and other related smart devices and related technologies. Most of the current predictive models are complex and not reliable despite numerous efforts in the past by the research community. The performance accuracy of the predictive models have not always been realised due to many factors such as complexity and class imbalance. Therefore there is a need to improve the predictive accuracy of current ensemble models and to enhance their applications and reliability and non-visual predictive tools. The research work presented in this thesis has adopted a pragmatic phased approach to propose and develop new ensemble models using multiple methods and validated the methods through rigorous testing and implementation in different phases. The first phase comprises of empirical investigations on standalone and ensemble algorithms that were carried out to ascertain their performance effects on complexity and simplicity of the classifiers. The second phase comprises of an improved ensemble model based on the integration of Extended Kalman Filter (EKF), Radial Basis Function Network (RBFN) and AdaBoost algorithms. The third phase comprises of an extended model based on early stop concepts, AdaBoost algorithm, and statistical performance of the training samples to minimize overfitting performance of the proposed model. The fourth phase comprises of an enhanced analytical multivariate logistic regression predictive model developed to minimize the complexity and improve prediction accuracy of logistic regression model. To facilitate the practical application of the proposed models; an ensemble non-invasive analytical tool is proposed and developed. The tool links the gap between theoretical concepts and practical application of theories to predict breast cancer survivability. The empirical findings suggested that: (1) increasing the complexity and topology of algorithms does not necessarily lead to a better algorithmic performance, (2) boosting by resampling performs slightly better than boosting by reweighting, (3) the prediction accuracy of the proposed ensemble EKF-RBFN-AdaBoost model performed better than several established ensemble models, (4) the proposed early stopped model converges faster and minimizes overfitting better compare with other models, (5) the proposed multivariate logistic regression concept minimizes the complexity models (6) the performance of the proposed analytical non-invasive tool performed comparatively better than many of the benchmark analytical tools used in predicting breast cancers and diabetics ailments. The research contributions to ensemble practice are: (1) the integration and development of EKF, RBFN and AdaBoost algorithms as an ensemble model, (2) the development and validation of ensemble model based on early stop concepts, AdaBoost, and statistical concepts of the training samples, (3) the development and validation of predictive logistic regression model based on breast cancer, and (4) the development and validation of a non-invasive breast cancer analytic tools based on the proposed and developed predictive models in this thesis. To validate prediction accuracy of ensemble models, in this thesis the proposed models were applied in modelling breast cancer survivability and diabetics’ diagnostic tasks. In comparison with other established models the simulation results of the models showed improved predictive accuracy. The research outlines the benefits of the proposed models, whilst proposes new directions for future work that could further extend and improve the proposed models discussed in this thesis

    Visual and semantic knowledge transfer for large scale semi-supervised object detection

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    Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large number of labeled bounding boxes, which are more difficult to obtain than image-level annotations. Previous work addresses this issue by transforming image-level classifiers into object detectors. This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We improve this previous work by incorporating knowledge about object similarities from visual and semantic domains during the transfer process. The intuition behind our proposed method is that visually and semantically similar categories should exhibit more common transferable properties than dissimilar categories, e.g. a better detector would result by transforming the differences between a dog classifier and a dog detector onto the cat class, than would by transforming from the violin class. Experimental results on the challenging ILSVRC2013 detection dataset demonstrate that each of our proposed object similarity based knowledge transfer methods outperforms the baseline methods. We found strong evidence that visual similarity and semantic relatedness are complementary for the task, and when combined notably improve detection, achieving state-of-the-art detection performance in a semi-supervised setting

    Machine learning ensemble method for discovering knowledge from big data

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    Big data, generated from various business internet and social media activities, has become a big challenge to researchers in the field of machine learning and data mining to develop new methods and techniques for analysing big data effectively and efficiently. Ensemble methods represent an attractive approach in dealing with the problem of mining large datasets because of their accuracy and ability of utilizing the divide-and-conquer mechanism in parallel computing environments. This research proposes a machine learning ensemble framework and implements it in a high performance computing environment. This research begins by identifying and categorising the effects of partitioned data subset size on ensemble accuracy when dealing with very large training datasets. Then an algorithm is developed to ascertain the patterns of the relationship between ensemble accuracy and the size of partitioned data subsets. The research concludes with the development of a selective modelling algorithm, which is an efficient alternative to static model selection methods for big datasets. The results show that maximising the size of partitioned data subsets does not necessarily improve the performance of an ensemble of classifiers that deal with large datasets. Identifying the patterns exhibited by the relationship between ensemble accuracy and partitioned data subset size facilitates the determination of the best subset size for partitioning huge training datasets. Finally, traditional model selection is inefficient in cases wherein large datasets are involved

    Towards holistic scene understanding:Semantic segmentation and beyond

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    This dissertation addresses visual scene understanding and enhances segmentation performance and generalization, training efficiency of networks, and holistic understanding. First, we investigate semantic segmentation in the context of street scenes and train semantic segmentation networks on combinations of various datasets. In Chapter 2 we design a framework of hierarchical classifiers over a single convolutional backbone, and train it end-to-end on a combination of pixel-labeled datasets, improving generalizability and the number of recognizable semantic concepts. Chapter 3 focuses on enriching semantic segmentation with weak supervision and proposes a weakly-supervised algorithm for training with bounding box-level and image-level supervision instead of only with per-pixel supervision. The memory and computational load challenges that arise from simultaneous training on multiple datasets are addressed in Chapter 4. We propose two methodologies for selecting informative and diverse samples from datasets with weak supervision to reduce our networks' ecological footprint without sacrificing performance. Motivated by memory and computation efficiency requirements, in Chapter 5, we rethink simultaneous training on heterogeneous datasets and propose a universal semantic segmentation framework. This framework achieves consistent increases in performance metrics and semantic knowledgeability by exploiting various scene understanding datasets. Chapter 6 introduces the novel task of part-aware panoptic segmentation, which extends our reasoning towards holistic scene understanding. This task combines scene and parts-level semantics with instance-level object detection. In conclusion, our contributions span over convolutional network architectures, weakly-supervised learning, part and panoptic segmentation, paving the way towards a holistic, rich, and sustainable visual scene understanding.Comment: PhD Thesis, Eindhoven University of Technology, October 202

    Compact Integration of Multi-Network Topology for Functional Analysis of Genes

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    The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet-unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node. Next, the high-dimensional topological patterns in individual networks are canonically represented using low-dimensional vectors, one per gene or protein. These vectors can then be plugged into off-the-shelf machine learning methods to derive functional insights about genes or proteins. We present tools based on Mashup that achieve state-of-the-art performance in three diverse functional inference tasks: protein function prediction, gene ontology reconstruction, and genetic interaction prediction. Mashup enables deeper insights into the struct ure of rapidly accumulating and diverse biological network data and can be broadly applied to other network science domains. Keywords: interactome analysis; network integration; heterogeneous networks; dimensionality reduction; network diffusion; gene function prediction; genetic interaction prediction; gene ontology reconstruction; drug response predictionNational Institutes of Health (U.S.) (Grant R01GM081871

    Genome-wide Protein-chemical Interaction Prediction

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    The analysis of protein-chemical reactions on a large scale is critical to understanding the complex interrelated mechanisms that govern biological life at the cellular level. Chemical proteomics is a new research area aimed at genome-wide screening of such chemical-protein interactions. Traditional approaches to such screening involve in vivo or in vitro experimentation, which while becoming faster with the application of high-throughput screening technologies, remains costly and time-consuming compared to in silico methods. Early in silico methods are dependant on knowing 3D protein structures (docking) or knowing binding information for many chemicals (ligand-based approaches). Typical machine learning approaches follow a global classification approach where a single predictive model is trained for an entire data set, but such an approach is unlikely to generalize well to the protein-chemical interaction space considering its diversity and heterogeneous distribution. In response to the global approach, work on local models has recently emerged to improve generalization across the interaction space by training a series of independant models localized to each predict a single interaction. This work examines current approaches to genome-wide protein-chemical interaction prediction and explores new computational methods based on modifications to the boosting framework for ensemble learning. The methods are described and compared to several competing classification methods. Genome-wide chemical-protein interaction data sets are acquired from publicly available resources, and a series of experimental studies are performed in order to compare the the performance of each method under a variety of conditions
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