1,720,954 research outputs found
Intelligent Adaptation of Ensemble Size in Data Streams Using Online Bagging
No full textIn this era of the Internet of Things and Big Data, a proliferation of connected devices continuously produce massive amounts of fast evolving streaming data. There is a need to study the relationships in such streams for analytic applications, such as network intrusion detection, fraud detection and financial forecasting, amongst other. In this setting, it is crucial to create data mining algorithms that are able to seamlessly adapt to temporal changes in data characteristics that occur in data streams. These changes are called concept drifts. The resultant models produced by such algorithms should not only be highly accurate and be able to swiftly adapt to changes. Rather, the data mining techniques should also be fast, scalable, and efficient in terms of resource allocation. It then becomes important to consider issues such as storage space needs and memory utilization. This is especially relevant when we aim to build personalized, near-instant models in a Big Data setting.
This research work focuses on mining in a data stream with concept drift, using an online bagging method, with consideration to the memory utilization. Our aim is to take an adaptive approach to resource allocation during the mining process. Specifically, we consider metalearning, where the models of multiple classifiers are combined into an ensemble, has been very successful when building accurate models against data streams. However, little work has been done to explore the interplay between accuracy, efficiency and utility. This research focuses on this issue. We introduce an adaptive metalearning algorithm that takes advantage of the memory utilization cost of concept drift, in order to vary the ensemble size during the data mining process. We aim to minimize the memory usage, while maintaining highly accurate models with a high utility.
We evaluated our method against a number of benchmarking datasets and compare our results against the state-of-the art. Return on Investment (ROI) was used to evaluate the gain in performance in terms of accuracy, in contrast to the time and memory invested. We aimed to achieve high ROI without compromising on the accuracy of the result. Our experimental results indicate that we achieved this goal
Temporal Deep Learning for Financial Time Series
No full textThe widespread usage of machine learning in different mainstream contexts has made deep learning the solution of choice in various domains, including finance. However, the real-world application of deep learning in the stock market is still emerging. When practitioners are applying machine learning to the stock market, there are three primary stages, namely (1) data/feature engineering, (2) price forecast, and (3) investment decision and strategy. Considerations across all three stages include the unique characteristics of financial time series data, the need to understand model predictions, and ease of use. Historically, deep learning is considered to not excel under these considerations, hence it is common to see classical statistical and traditional machine learning methods in use by market practitioners. In this Ph.D. thesis, we focus on advancing this area of research by combining the state-of-the-art in both financial statistics and deep learning.
In this thesis, we put financial-specific data attributes into consideration to produce novel algorithms based on the temporal Transformer deep learning architecture, a state-of-the-art deep learning approach that draws dependencies between data sequences using a mechanism called attention. By introducing temporal Transformers focused on the financial domain, we illustrate its predictive use case for financial time series. We also incorporate discussions on explainable AI (XAI), to mitigate the black-box nature often associated with such deep learning algorithms.
We address the first two stages by introducing similarity embedded temporal Transformer (SeTT) and run-similarity embedded temporal Transformer (r-SeTT) algorithms that combine temporal Transformer architecture with time series forecasting and statistical principles. We employ similarity vectors generated from historical trends across different financial instruments that are used to adjust the weight of the temporal Transformer model during the training process. This approach takes advantage of the conditional heteroscedasticity in financial time series, by using the historical volatility in combination with the attention mechanism in a temporal Transformer deep neural network architecture. Our experimentation shows that by focusing on the historical windows that are most similar to the current window in the attention-tuning process, we outperform both classical financial models and the baseline temporal Transformer model in terms of predictive performance.
To effectively utilize an extended history of financial time series data, we further develop an ensemble algorithm called windowing ensemble of temporal Transformers (WETT). Our ensemble algorithm leverages a combination of base models generated from sliding windows of historical timeframes, with additional weight initialization diversification options for a complete experimentation regime. By decomposing the constituent time series of the extended timeframe, we optimize the utilization of the series for financial deep learning. This simplifies the training process while achieving better performance, particularly when accounting for the non-constant variance of financial time series.
To address the last stage of applying complex deep learning architectures to the financial market, we examined advancements in XAI and its application in facilitating investment decision-making processes. Our discussion centers on incorporating XAI into our model development pipeline, through the use of surrogate models. This step aims not only to augment our comprehension of the temporal Transformer model but also to improve the model's predictive capabilities by assessing the effectiveness of the input features
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
- …
