Cross-validation based K nearest neighbor imputation for software quality datasets: An empirical study

Being able to predict software quality is essential, but also it pose significant challenges in software engineering. Historical software project datasets are often being utilized together with various machine learning algorithms for fault-proneness classification. Unfortunately, the missing values in datasets have negative impacts on the estimation accuracy and therefore, could lead to inconsistent results. As a method handling missing data, K nearest neighbor (KNN) imputation gradually gains acceptance in empirical studies by its exemplary performance and simplicity. To date, researchers still call for optimized parameter setting for KNN imputation to further improve its performance. In the work, we develop a novel incomplete-instance based KNN imputation technique, which utilizes a cross-validation scheme to optimize the parameters for each missing value. An experimental assessment is conducted on eight quality datasets under various missingness scenarios. The study also compared the proposed imputation approach with mean imputation and other three KNN imputation approaches. The results show that our proposed approach is superior to others in general. The relatively optimal fixed parameter settings for KNN imputation for software quality data is also determined. It is observed that the classification accuracy is improved or at least maintained by using our approach for missing data imputation

Missing Data Imputation Using Machine Learning and Natural Language Processing for Clinical Diagnostic Codes

Imputation of missing data is a common application in supervised classification problems, where the feature matrix of the training dataset has various degrees of missingness. Most of the former studies do not take into account the presence of the class label in the classification problem with missing data. A widely used solution to this problem is missing data imputation based on the lazy learning technique, k-Nearest Neighbor (KNN) approach. We work on a variant of this imputation algorithm using Gray's distance and Mutual Information (MI), called Class-weighted Gray's $k$-Nearest Neighbor (CGKNN) approach. Gray's distance works well with heterogeneous mixed-type data with missing instances, and we weigh distance with mutual information (MI), a measure of feature relevance, between the features and the class label. This method performs better than traditional methods for classification problems with mixed data, as shown in simulations and applications on University of California, Irvine (UCI) Machine Learning datasets (http://archive.ics.uci.edu/ml/index.php). Data being lost to follow up is a common problem in longitudinal data, especially if it involves multiple visits over a long period of time. If the outcome of interest is present in each time point, despite missing covariates due to follow-up (like outcome ascertained through phone calls), then random forest imputation would be a good imputation technique for the missing covariates. The missingness of the data involves more complicated interactions over time since most of the covariates and the outcome have repeated measurements over time. Random forests are a good non-parametric learning technique which captures complex interactions between mixed type data. We propose a proximity imputation and missForest type covariate imputation with random splits while building the forest. The performance of the imputation techniques used is compared to existing techniques in various simulation settings. The Atherosclerosis Risk in Communities (ARIC) Study Cohort is a longitudinal study which started in 1987-1989 to collect data on participants across 4 states in the USA, aimed at studying the factors behind heart diseases. We consider patients at the 5th visit (occurred in 2013) and enrolled in continuous Medicare Fee-For-Service (FFS) insurance in the last 6 months prior to their visit so that their hospitalization diagnostic (ICD) codes are available. Our aim is to characterize the hospitalization of patients having cognitive status ascertainment (classified into dementia, mild cognitive disorder or no cognitive disorder) in the 5th visit. Diagnostic codes for inpatient and outpatient visits identified from CMS (Centers for Medicare \& Medicaid Services) Medicare FFS data linked with ARIC participant data are stored in the form of International Classification of Diseases and related health problems (ICD) codes. We treat these codes as a bag-of-words model to apply text mining techniques and get meaningful cluster of ICD codes.Doctor of Philosoph

The Effect of Using Data Pre-Processing by Imputations in Handling Missing Values

The evolution of big data analytics through machine learning and artificial intelligence techniques has caused organizations in a wide range of sectors including health, manufacturing, e-commerce, governance, and social welfare to realize the value of massive volumes of data accumulating on web-based repositories daily. This has led to the adoption of data-driven decision models; for example, through sentiment analysis in marketing where produces leverage customer feedback and reviews to develop customer-oriented products. However, the data generated in real-world activities is subject to errors resulting from inaccurate measurements or fault input devices, which may result in the loss of some values. Missing attribute/variable values make data unsuitable for decision analytics due to noises and inconsistencies that create bias. The objective of this paper was to explore the problem of missing data and develop an advanced imputation model based on Machine Learning and implemented on K-Nearest Neighbor (KNN) algorithm in R programming language as an approach to handle missing values. The methodology used in this paper relied on the applying advanced machine learning algorithms with high-level accuracy in pattern detection and predictive analytics on the existing imputation techniques, which handle missing values by random replacement or deletion..  According to the results, advanced imputation technique based on machine learning models replaced missing values from a dataset with 89.5% accuracy. The experimental results showed that pre-processing by imputation delivers high-level performance efficiency in handling missing data values. These findings are consistent with the key idea of paper, which is to explore alternative imputation techniques for handling missing values to improve the accuracy and reliability of decision insights extracted from datasets

Deep Representation-aligned Graph Multi-view Clustering for Limited Labeled Multi-modal Health Data

Today, many fields are characterised by having extensive quantities of data from a wide range of dissimilar sources and domains. One such field is medicine, in which data contain exhaustive combinations of spatial, temporal, linear, and relational data. Often lacking expert-assessed labels, much of this data would require analysis within the fields of unsupervised or semi-supervised learning. Thus, reasoned by the notion that higher view-counts provide more ways to recognise commonality across views, contrastive multi-view clustering may be utilised to train a model to suppress redundancy and otherwise medically irrelevant information. Yet, standard multi-view clustering approaches do not account for relational graph data. Recent developments aim to solve this by utilising various graph operations including graph-based attention. And within deep-learning graph-based multi-view clustering on a sole view-invariant affinity graph, representation alignment remains unexplored. We introduce Deep Representation-Aligned Graph Multi-View Clustering (DRAGMVC), a novel attention-based graph multi-view clustering model. Comparing maximal performance, our model surpassed the state-of-the-art in eleven out of twelve metrics on Cora, CiteSeer, and PubMed. The model considers view alignment on a sample-level by employing contrastive loss and relational data through a novel take on graph attention embeddings in which we use a Markov chain prior to increase the receptive field of each layer. For clustering, a graph-induced DDC module is used. GraphSAINT sampling is implemented to control our mini-batch space to capitalise on our Markov prior. Additionally, we present the MIMIC pleural effusion graph multi-modal dataset, consisting of two modalities registering 3520 chest X-ray images along with two static views registered within a one-day time frame: vital signs and lab tests. These making up the, in total, three views of the dataset. We note a significant improvement in terms of separability, view mixing, and clustering performance comparing DRAGMVC to preceding non-graph multi-view clustering models, suggesting a possible, largely unexplored use case of unsupervised graph multi-view clustering on graph-induced, multi-modal, and complex medical data

Delivering Reliable AI to Clinical Contexts: Addressing the Challenge of Missing Data

Clinical data are essential in the medical domain, ensuring quality of care and improving decision-making. However, their heterogeneous and incomplete nature leads to an ubiquity of data quality problems, particularly missing values. Inevitable challenges arise in delivering reliable Decision Support Systems (DSSs), as missing data yield negative effects on the learning process of Machine Learning models. The interest in developing missing value imputation strategies has been growing, in an endeavour to overcome this issue. This dissertation aimed to study missing data and their relationships with observed values, and to lateremploy that information in a technique that addresses the predicaments posed by incomplete datasets in real-world scenarios. Moreover, the concept of correlation was explored within the context of missing value imputation, a promising but rather overlooked approach in biomedical research. First, a comprehensive correlational study was performed, which considered key aspects from missing data analysis. Afterwards, the gathered knowledge was leveraged to create three novel correlation-based imputation techniques. Thesewere not only validated on datasets with a controlled and synthetic missingness, but also on real-world medical datasets. Their performance was evaluated against competing imputation methods, both traditional and state-of-the-art. The contributions of this dissertation encompass a systematic view of theoretical concepts regarding the analysis and handling of missing values. Additionally, an extensive literature review concerning missing data imputation was conducted, which comprised a comparative study of ten methods under diverse missingness conditions. The proposed techniques exhibited similar results when compared to their competitors, sometimes even superior in terms of imputation precision and classification performance, evaluated through the Mean Absolute Error and the Area Under the Receiver Operating Characteristic curve, respectively. Therefore, this dissertation corroborates the potential of correlation to improve the robustness of DSSs to missing values, and provides answers to current flaws shared by correlation-based imputation strategies in real-world medical problems.Dados clínicos são essenciais para assegurar cuidados médicos de qualidade e melhorar a tomada de decisões. Contudo, a sua natureza heterogénea e incompleta cria uma ubiquidade de problemas de qualidade, nomeadamente pela existência de valores em falta. Esta condição origina desafios inevitáveis para a disponibilização de Sistemas de Apoio à Decisão (SADs) fiáveis, já que dados em falta acarretam efeitos negativos no treino de modelos de Aprendizagem Automática. O interesse no desenvolvimento de estratégias de imputação de valores em falta tem vindo a crescer, num esforço para superar esta adversidade. Esta dissertação visou estudar o problema dos dados em falta através das relações que estes apresentam com os valores observados. Esta informação foi depois utilizada no desenvolvimento de técnicas para colmatar os problemas impostos por dados incompletos em cenários reais. Ademais, o conceito de correlação foi explorado no contexto da imputação de valores em falta, já que, apesar de promissor, tem vindo a ser negligenciado em investigação biomédica. Em primeiro lugar, foi realizado um estudo correlacional abrangente que contemplou aspetos fundamentais da análise de dados em falta. Posteriormente, o conhecimento recolhido foi aplicado na criação de três novas técnicas de imputação baseadas na correlação. Estas foram validadas não só em conjuntos de dados com incompletude controlada e sintética, mas também em conjuntos de dados médicos reais. O seu desempenho foi avaliado e comparado a métodos de imputação tanto tradicionais como de estado-de-arte. As contribuições desta dissertação passam pela sistematização de conceitos teóricos relativos à análise e tratamento de dados em falta. Adicionalmente, realizou-se uma extensa revisão da literatura referente à imputação de dados, que compreendeu um estudo comparativo de dez métodos sob diversas condições de incompletude. As técnicas propostas exibiram resultados semelhantes aos dos restantes métodos, por vezes até superiores em termos de precisão da imputação e de performance da classificação. Assim, esta dissertação corrobora o potencial da utilização da correlação na melhoria da robustez de SADs a dados em falta, e fornece respostas a algumas das atuais falhas partilhadas por estratégias de imputação baseadas em correlação quando aplicadas a casos médicos reais

Imaging biomarkers extraction and classification for Prion disease

Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by inheritance of prion protein gene mutations or exposure to prions. To date, there are no accurate imaging biomarkers that can be used to predict the future diagnosis of a subject or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the large heterogeneity of phenotypes of prion disease and the lack of a consistent spatial pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of the human form of prion disease. Using a tailored framework, I extracted quantitative imaging biomarkers for characterisation of patients with Prion diseases. Following the extraction of patient-specific imaging biomarkers from multiple images, I implemented a Gaussian Process approach to correlated symptoms with disease types and stages. The model was used on three different tasks: diagnosis, differential diagnosis and stratification, addressing an unmet need to automatically identify patients with or at risk of developing Prion disease. The work presented in this thesis has been extensively validated in a unique Prion disease cohort, comprising both the inherited and sporadic forms of the disease. The model has shown to be effective in the prediction of this illness. Furthermore, this approach may have used in other disorders with heterogeneous imaging features, being an added value for the understanding of neurodegenerative diseases. Lastly, given the rarity of this disease, I also addressed the issue of missing data and the limitations raised by it. Overall, this work presents progress towards modelling of Prion diseases and which computational methodologies are potentially suitable for its characterisation

Deficient data classification with fuzzy learning

This thesis first proposes a novel algorithm for handling both missing values and imbalanced data classification problems. Then, algorithms for addressing the class imbalance problem in Twitter spam detection (Network Security Problem) have been proposed. Finally, the security profile of SVM against deliberate attacks has been simulated and analysed.<br /