9 research outputs found
A Review of Missing Data Handling Techniques for Machine Learning
Real-world data are commonly known to contain missing values, and consequently affect the performance of most machine learning algorithms adversely when employed on such datasets. Precisely, missing values are among the various challenges occurring in real-world data. Since the accuracy and efficiency of machine learning models depend on the quality of the data used, there is a need for data analysts and researchers working with data, to seek out some relevant techniques that can be used to handle these inescapable missing values. This paper reviews some state-of-art practices obtained in the literature for handling missing data problems for machine learning. It lists some evaluation metrics used in measuring the performance of these techniques. This study tries to put these techniques and evaluation metrics in clear terms, followed by some mathematical equations. Furthermore, some recommendations to consider when dealing with missing data handling techniques were provided
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Phenotyping with Partially Labeled, Partially Observed Data
Identifying a group of individuals that share a common set of characteristics is a conceptually simple task, which is often difficult in practice. Such phenotyping problems emerge in various settings, including the analysis of clinical data. In this setting, phenotyping is often stymied by persistent data quality issues. These include a lack of reliable labels to indicate the presence of absence of characteristics of interest, and significant missingness in observed variables.
This dissertation introduces methods for learning phenotypes when the data contain missing values (partially observed) and labels are scarce (partially labeled). Aim 1 utilizes an unsupervised probabilistic graphical model to learn phenotypes from partially observed data. Aim 2 introduces a related semi-supervised probabilistic graphical model for learning phenotypes from partially labeled clinical data. Finally, Aim 3 describes a method for training deep generative models when the training data contain missing values. The algorithm is then applied in a semi-supervised setting where it accounts for partially labeled data as well
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 /
Fuelling the zero-emissions road freight of the future: routing of mobile fuellers
The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios
Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm
Abstract— Online transportation has become a basic
requirement of the general public in support of all activities to go
to work, school or vacation to the sights. Public transportation
services compete to provide the best service so that consumers
feel comfortable using the services offered, so that all activities
are noticed, one of them is the search for the shortest route in
picking the buyer or delivering to the destination. Node
Combination method can minimize memory usage and this
methode is more optimal when compared to A* and Ant Colony
in the shortest route search like Dijkstra algorithm, but can’t
store the history node that has been passed. Therefore, using
node combination algorithm is very good in searching the
shortest distance is not the shortest route. This paper is
structured to modify the node combination algorithm to solve the
problem of finding the shortest route at the dynamic location
obtained from the transport fleet by displaying the nodes that
have the shortest distance and will be implemented in the
geographic information system in the form of map to facilitate
the use of the system.
Keywords— Shortest Path, Algorithm Dijkstra, Node
Combination, Dynamic Location (key words
The Routledge Handbook of Refugee Narratives
This Handbook presents a transnational and interdisciplinary study of refugee narratives, broadly defined. Interrogating who can be considered a refugee and what constitutes a narrative, the thirty-eight chapters included in this collection encompass a range of forcibly displaced subjects, a mix of geographical and historical contexts, and a variety of storytelling modalities. Analyzing novels, poetry, memoirs, comics, films, photography, music, social media, data, graffiti, letters, reports, eco-design, video games, archival remnants, and ethnography, the individual chapters counter dominant representations of refugees as voiceless victims. Addressing key characteristics and thematics of refugee narratives, this Handbook examines how refugee cultural productions are shaped by and in turn shape socio-political landscapes. It will be of interest to researchers, teachers, students, and practitioners committed to engaging refugee narratives in the contemporary moment.
The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND)] 4.0 license