7,106 research outputs found
SME credit application, a text classification approach
Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMDuring the SME credit application process a credit expert will give a specific recommendation to the
credit commercial advisor. This recommendation can be classified as positive, negative or partial. This
project aims to construct a text classifier model in order to give the recommendation text one of the
categories mentioned before. To achieve this, two models are tested using state-of-the-art
architecture called BERT proposed by Google in 2019.
The first model will use single sentence BERT classification model as proposed by Google. The second
model will use SBERT architecture, where BERT embedding model will be fine-tuned for the specific
task, a max-pooling layer is added to extract a fixed size vector for all the document and work under
fully connected network architecture. Results show that the second approach got better results
regarding accuracy, precision and recall. Despite of the bunch of limitations of computational capacity,
limited number of tagged examples and BERT maximum sequence length the model show a good first
approach to solve the current problem
Boundedly Rational Decision Emergence - A General Perspective and Some Selective Illustrations
A general framework is described specifying how boundedly rational decision makers generate their choices. Starting from a "Master Module" which keeps an inventory of previously successful and unsuccessful routines several submodules can be called forth which either allow one to adjust behavior (by "Learning Module" and "Adaptation Procedure") or to generate new decision routines (by applying "New Problem Solver"). Our admittedly bold attempt is loosely related to some stylized experimental results.
Collaborative-demographic hybrid for financial: product recommendation
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM
processes, several financial institutions are striving to leverage customer data and integrate insights
regarding customer behaviour, needs, and preferences into their marketing approach. As decision
support systems assisting marketing and commercial efforts, Recommender Systems applied to the
financial domain have been gaining increased attention. This thesis studies a Collaborative-
Demographic Hybrid Recommendation System, applied to the financial services sector, based on real
data provided by a Portuguese private commercial bank. This work establishes a framework to support
account managers’ advice on which financial product is most suitable for each of the bank’s corporate
clients. The recommendation problem is further developed by conducting a performance comparison
for both multi-output regression and multiclass classification prediction approaches. Experimental
results indicate that multiclass architectures are better suited for the prediction task, outperforming
alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass
Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming
algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving
corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study
provides important contributions for positioning the bank’s commercial efforts around customers’
future requirements. By allowing for a better understanding of customers’ needs and preferences, the
proposed Recommender allows for more personalized and targeted marketing contacts, leading to
higher conversion rates, corporate profitability, and customer satisfaction and loyalty
Computational Intelligence for the Micro Learning
The developments of the Web technology and the mobile devices have blurred the time and space boundaries of people’s daily activities, which enable people to work, entertain, and learn through the mobile device at almost anytime and anywhere. Together with the life-long learning requirement, such technology developments give birth to a new learning style, micro learning. Micro learning aims to effectively utilise learners’ fragmented spare time and carry out personalised learning activities. However, the massive volume of users and the online learning resources force the micro learning system deployed in the context of enormous and ubiquitous data. Hence, manually managing the online resources or user information by traditional methods are no longer feasible. How to utilise computational intelligence based solutions to automatically managing and process different types of massive information is the biggest research challenge for realising the micro learning service. As a result, to facilitate the micro learning service in the big data era efficiently, we need an intelligent system to manage the online learning resources and carry out different analysis tasks. To this end, an intelligent micro learning system is designed in this thesis.
The design of this system is based on the service logic of the micro learning service. The micro learning system consists of three intelligent modules: learning material pre-processing module, learning resource delivery module and the intelligent assistant module. The pre-processing module interprets the content of the raw online learning resources and extracts key information from each resource. The pre-processing step makes the online resources ready to be used by other intelligent components of the system. The learning resources delivery module aims to recommend personalised learning resources to the target user base on his/her implicit and explicit user profiles. The goal of the intelligent assistant module is to provide some evaluation or assessment services (such as student dropout rate prediction and final grade prediction) to the educational resource providers or instructors. The educational resource providers can further refine or modify the learning materials based on these assessment results
Approximate encoding of quantum states using shallow circuits
A common requirement of quantum simulations and algorithms is the preparation
of complex states through sequences of 2-qubit gates. For a generic quantum
state, the number of gates grows exponentially with the number of qubits,
becoming unfeasible on near-term quantum devices. Here, we aim at creating an
approximate encoding of the target state using a limited number of gates. As a
first step, we consider a quantum state that is efficiently represented
classically, such as a one-dimensional matrix product state. Using tensor
network techniques, we develop an optimization algorithm that approaches the
optimal implementation for a fixed number of gates. Our algorithm runs
efficiently on classical computers and requires a polynomial number of
iterations only. We demonstrate the feasibility of our approach by comparing
optimal and suboptimal circuits on real devices. We, next, consider the
implementation of the proposed optimization algorithm directly on a quantum
computer and overcome inherent barren plateaus by employing a local cost
function rather than a global one. By simulating realistic shot noise, we
verify that the number of required measurements scales polynomially with the
number of qubits. Our work offers a universal method to prepare target states
using local gates and represents a significant improvement over known
strategies.Comment: 9 + 6 page
An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm—An Application for Aerosol Particle Number Concentrations
Time-series prediction is an important area that inspires numerous research disciplines for various applications, including air quality databases. Developing a robust and accurate model for time-series data becomes a challenging task, because it involves training different models and optimization. In this paper, we proposed and tested three machine learning techniques—recurrent neural networks (RNN), heuristic algorithm and ensemble learning—to develop a predictive model for estimating atmospheric particle number concentrations in the form of a time-series database. Here, the RNN included three variants—Long-Short Term Memory, Gated Recurrent Network, and Bi-directional Recurrent Neural Network—with various configurations. A Genetic Algorithm (GA) was then used to find the optimal time-lag in order to enhance the model’s performance. The optimized models were used to construct a stacked ensemble model as well as to perform the final prediction. The results demonstrated that the time-lag value can be optimized by using the heuristic algorithm; consequently, this improved the model prediction accuracy. Further improvement can be achieved by using ensemble learning that combines several models for better performance and more accurate predictions
An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm—An Application for Aerosol Particle Number Concentrations
Time-series prediction is an important area that inspires numerous research disciplines for various applications, including air quality databases. Developing a robust and accurate model for time-series data becomes a challenging task, because it involves training different models and optimization. In this paper, we proposed and tested three machine learning techniques—recurrent neural networks (RNN), heuristic algorithm and ensemble learning—to develop a predictive model for estimating atmospheric particle number concentrations in the form of a time-series database. Here, the RNN included three variants—Long-Short Term Memory, Gated Recurrent Network, and Bi-directional Recurrent Neural Network—with various configurations. A Genetic Algorithm (GA) was then used to find the optimal time-lag in order to enhance the model’s performance. The optimized models were used to construct a stacked ensemble model as well as to perform the final prediction. The results demonstrated that the time-lag value can be optimized by using the heuristic algorithm; consequently, this improved the model prediction accuracy. Further improvement can be achieved by using ensemble learning that combines several models for better performance and more accurate predictions
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