When a knowledge Discovery from Data (KDD) (Fayyad, Piatetsky-Shapiro, & Smyth, 1996) process is being applied to get knowledge, several methods could be used (Gibert, et al., 2018). A simple and fast way to obtai...
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ISBN:
(纸本)9781643685434
When a knowledge Discovery from Data (KDD) (Fayyad, Piatetsky-Shapiro, & Smyth, 1996) process is being applied to get knowledge, several methods could be used (Gibert, et al., 2018). A simple and fast way to obtain preliminary insights from data before using KDD models is by generating a basic descriptive analysis. It is one of the most popular ways to describe experimental data and should be the beginning of all data projects. Nevertheless some of the main knowledge that can be extracted in a descriptive analysis is hidden due to underlying multivariate structures which could be elicited through multivariate analysis techniques. Moreover, the domain expert is key for a proper interpretation of descriptive results. At the same time, there is a lack of automatic reporting techniques that can report and help in the interpretation of complex patterns and the use of advanced multivariate techniques. This paper shows the tool developed to generate automatic interpretation of Multiple Correspondence Analysis (MCA) and Principal Components Analysis (PCA) by using RMarkdown. This tool generates a Word document which contains the automatic interpretation of the results, built on the basis of regular expressions ellaborating over the R analytical outputs (either numerical or graphical results). The proposal is being applied with some real data, like INSESS database on social vulnerabilities of the Catalan population. In conclusion, the developed tool contributes to facilitate the factorial methods results, avoiding the misinterpretation of the results and the involuntary skipping of conclusions due to the large amount of knowledge that can be extracted from a complete factorial analysis. Also, this software enables non-expert users to read multivariate analysis results in a friendly way. Moreover, this tool saves time in the interpretation step and is a basis to support the expert to start the report with the results, even the output of the software could become the report or
This study focuses on developing an intelligent decision support system (IDSS) that helps a human operator make data-driven decisions. To put IDSS in production, it is necessary to develop two additional components: o...
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Difficulties in replication and reproducibility of empirical evidences in machinelearning research have become a prominent topic in recent years. Ensuring that machinelearning research results are sound and reliable...
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The A.I. disruption and the need to compete on innovation are impacting cities that have an increasing necessity to become innovation hotspots. However, without proven solutions, experimentation, often unsuccessful, i...
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machinelearning (ML) models trained on biased data can reproduce and even amplify these biases. Since such models are deployed to make decisions that can affect people's lives, ensuring their fairness is critical...
machinelearning (ML) models trained on biased data can reproduce and even amplify these biases. Since such models are deployed to make decisions that can affect people's lives, ensuring their fairness is critical. One approach to mitigate possible unfairness of ML models is to map the input data into a less-biased new space by means of training the model on fair representations. Several methods based on adversarial learning have been proposed to learn fair representation by fooling an adversary in predicting the sensitive attribute (e.g., gender or race). However, adversarial-based learning can be too difficult to optimize in practice; besides, it penalizes the utility of the representation. Hence, in this research effort we train bias-free representations from the input data by inducing a uniform distribution over the sensitive attributes in the latent space. In particular, we propose a probabilistic framework that learns these representations by enforcing the correct reconstruction of the original data, plus the prediction of the attributes of interest while eliminating the possibility of predicting the sensitive ones. Our method leverages the inability of Deep Neural Networks (DNNs) to generalize when trained on a noisy label space to regularize the latent space. We use a network head that predicts a noisy version of the sensitive attributes in order to increase the uncertainty of their predictions at test time. Our experiments in two datasets demonstrated that the proposed model significantly improves fairness while maintaining the prediction accuracy of downstream tasks.
Underwater target localization using range-only and single-beacon (ROSB) techniques with autonomous vehicles has been used recently to improve the limitations of more complex methods, such as long baseline and ultra-s...
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The matrix-based Rényi’s entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This intriguing property makes it wid...
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The recently developed matrix-based Rényi's αorder entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi-definite (PSD) matrices in reproducing kernel H...
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We introduce SPFlow, an open-source Python library providing a simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs). The libr...
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Background: The increasing number of people with dementia (PwD) drives research exploring Web-based support interventions to provide effective care for larger populations. In this concept, a Web-based platform (CAREGI...
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Background: The increasing number of people with dementia (PwD) drives research exploring Web-based support interventions to provide effective care for larger populations. In this concept, a Web-based platform (CAREGIVERSPRO-MMD, 620911) was designed to (1) improve the quality of life for PwD, (2) reduce caregiver burden, (3) reduce the financial costs for care, and (4) reduce administration time for health and social care professionals. Objective: The objective of this study was to evaluate the usability and usefulness of CAREGIVERSPRO-MMD platform for PwD or mild cognitive impairment (MCI), informal caregivers, and health and social care professionals with respect to a wider strategy followed by the project to enhance the user-centered approach. A secondary aim of the study was to collect recommendations to improve the platform before the future pilot study. Methods: A mixed methods design was employed for recruiting PwD or MCI (N=24), informal caregivers (N=24), and professionals (N=10). Participants were asked to rate their satisfaction, the perceived usefulness, and ease of use of each function of the platform. Qualitative questions about the improvement of the platform were asked when participants provided low scores for a function. Testing occurred at baseline and 1 week after participants used the platform. The dropout rate from baseline to the follow-up was approximately 10% (6/58). Results: After 1 week of platform use, the system was useful for 90% (20.75/23) of the caregivers and for 89% (5.36/6) of the professionals. When users responded tomore than 1 question per platform function, the mean of satisfied users per function was calculated. These user groups also provided positive evaluations for the ease of use (caregivers: 82%, 18.75/23;professionals: 97%, 5.82/6) and their satisfaction with the platform (caregivers: 79%, 18.08/23;professionals: 73%, 4.36/6). Ratings from PwD were lower than the other groups for usefulness (57%, 13/23), ease of use (41%
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