Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 101 for “"feature engineering"”.
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AutoFE : efficient and robust automated feature engineering
Feature engineering is the key to building highly successful machine learning models. We present AutoFE, a system designed to automate feature engineering. AutoFE generates a large set of new interpretable features by combining information in the original features. Given an augmented dataset, it …
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On pruning and feature engineering in Random Forests.
Random Forest (RF) is an ensemble classification technique that was developed by Leo Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for optimizing RF further by enhancing and …
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SigPro: Enabling Subject Matter Expert Guidance in Feature Engineering
… this thesis, we detail developments to SigPro, a feature engineering library in Python guided by Subject Matter Experts (SMEs). SigPro includes a suite of data processing building blocks, or primitives, as well as an algorithm to combine primitives to form feature engineering pipelines. These …
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Effective detection of security compromises in enterprises using feature engineering
The student, Jiayi Duan, submitted this Thesis for approval on 2016-12-07 at 23:45.
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Understanding BITCOIN Market Mechanics Using Feature Engineering, Data Modeling, and Forecasting Methods
… the BTC market mechanics are simulated using a feature set of endogenous and exogenous variables. It is necessary to recognize patterns within images of time-series data charts using deep learning, as shown in Article 3. Existing forecasting models fall short of providing a robust model that …
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Graph feature engineering and coordinate-based learning for transferable and energy-efficient artificial intelligence
… The first contribution is the Network Feature Embedding (NFE) pipeline, which integrates diffusion-based, positional, and structural descriptors into a unified representation for node classification. The second contribution is the Topology Coordinate-Driven Random Forests (TC-DRF) …
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Exploring Machine Learning, Feature Engineering, and Explainability to Constrain Spica’s Apsidal Constant through MESA Simulations
… of each ML and DL method and determine which features most influence the apsidal constant through feature engineering and explainability methods. Lastly, the research seeks to develop a weighted fusion approach, leveraging an ensemble voting regressor capable of predicting the apsidal constant …
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A new feature engineering framework for financial cyber fraud detection using machine learning and deep learning
… This thesis looks at the holistic views of feature engineering for classification and machine learning (ML) and deep learning (DL) algorithms for fraud detection to understand their capabilities and how to deal with input data in each algorithm. And then, proposes a new feature engineering …
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Mining Help Desk Emails for Problem Domain Identification and Email Feature Engineering for Routing Incoming Emails
This work also outlines different pre-processing steps done on email from the help desk domain, such as removal of different types of noise elements and removal of stop words using a customized stop word list, and the outcome of each of these pre-processing steps.
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Scaling collaborative open data science
… fundamental units of contribution. I focus on feature engineering, structuring contributions as the creation of independent units of feature function source code. This then facilitates the integration of many submissions by diverse collaborators into a single, unified, machine learning model, …
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An end-to-end online quality prediction system for ultrasonic metal welding based on deep learning
… and not involving tedious data preprocessing and feature engineering. The effectiveness of the proposed method is shown using real-world data generated from a UMW process. A comparative case study is presented to compare three data fusion strategies (early fusion, middle fusion, and late fusion) …
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Machine Learning Classification of Gas Chromatography Data
… for enhancing the performance of ML algorithms. Feature Selection is a technique for improving performance by using a specific subset of the data. Feature Engineering is a technique to transform the data to make processing more effective. Data Fusion is a technique which combines multiple sources …
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Classifying advanced malware into families based on instruction link analysis
… to automatically spot malicious file. A lot of feature engineering approaches are explored to improve the performance of detection/classification system if feature engineering approach provides sufficient information of malware type for clustering purposes, then this indicates the possibility of …
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Collaborative, open, and automated data science
… model for the collaborative development of feature engineering pipelines, and is the first collaborative feature engineering framework. Using Ballet as a probe, we conduct a detailed case study analysis of an open-source personal income prediction project in order to better understand data …
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An Evaluation of Text Classification Methods for Literary Study
… to this area---SVM and naive Bayes select top features in different frequency ranges; stemming might harm feature selection methods. These experiment results provide new insights to the relation between classification methods, feature engineering options and non-topic document properties. They …
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