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On the Effectiveness of Convolutional Autoencoders on Image-Based Personalized Recommender Systems
(MDPI AG, 2020-08-19)
[Abstract]
Over the years, the success of recommender systems has become remarkable. Due to the massive arrival of options that a consumer can have at his/her reach, a collaborative environment was generated, where users ...
A comparison of performance of K-complex classification methods using feature selection
(2016-01-20)
[Abstract] The main objective of this work is to obtain a method that achieves the best accuracy results with a low false positive rate in the classification of K-complexes, a kind of transient waveform found in the ...
Low-Precision Feature Selection on Microarray Data: An Information Theoretic Approach
(Springer, 2022)
[Abstract] The number of interconnected devices, such as personal wearables, cars, and smart-homes, surrounding us every day has recently increased. The Internet of Things devices monitor many processes, and have the ...
Interpretable market segmentation on high dimension data
(M D P I AG, 2018-09-17)
[Abstract] Obtaining relevant information from the vast amount of data generated by interactions in a market or, in general, from a dyadic dataset, is a broad problem of great interest both for industry and academia. Also, ...
A scalable saliency-based feature selection method with instance-level information
(Elsevier, 2019-11)
[Abstract]: Classic feature selection techniques remove irrelevant or redundant features to achieve a subset of relevant features in compact models that are easier to interpret and so improve knowledge extraction. Most ...
Wavefront Marching Methods: A Unified Algorithm to Solve Eikonal and Static Hamilton-Jacobi Equations
(IEEE, 2019-12)
[Abstract]: This paper presents a unified propagation method for dealing with both the classic Eikonal equation, where the motion direction does not affect the propagation, and the more general static Hamilton-Jacobi ...
Regression Tree Based Explanation for Anomaly Detection Algorithm
(MDPI AG, 2020-08-18)
[Abstract]
This work presents EADMNC (Explainable Anomaly Detection on Mixed Numerical and Categorical spaces), a novel approach to address explanation using an anomaly detection algorithm, ADMNC, which provides accurate ...
Feature Selection With Limited Bit Depth Mutual Information for Embedded Systems
(MDPI AG, 2018-09-17)
[Abstract] Data is growing at an unprecedented pace. With the variety, speed and volume of data flowing through networks and databases, newer approaches based on machine learning are required. But what is really big in Big ...
Scalable Feature Selection Using ReliefF Aided by Locality-Sensitive Hashing
(Wiley, 2021)
[Abstract] Feature selection algorithms, such as ReliefF, are very important for processing high-dimensionality data sets. However, widespread use of popular and effective such algorithms is limited by their computational ...
Machine Learning Techniques to Predict Different Levels of Hospital Care of CoVid-19
(Springer, 2022)
[Abstract] In this study, we analyze the capability of several state of the art machine learning methods to predict whether patients diagnosed with CoVid-19 (CoronaVirus disease 2019) will need different levels of hospital ...