Learning systems have been focused on creating models capable of obtaining the best results in error metrics. Recently, the focus has shifted to improvement in the interpretation and explanation of the results. The need for interpretation is greater when these models are used to support decision making. In some areas, this becomes an indispensable requirement, such as in medicine. The goal of this study was to define a simple process to construct a system that could be easily interpreted based on two principles: (1) reduction of attributes without degrading the performance of the prediction systems and (2) selecting a technique to interpret the final prediction system. To describe this process, we selected a problem, predicting cardiovascular disease, by analyzing the well-known Statlog (Heart) data set from the University of California’s Automated Learning Repository. We analyzed the cost of making predictions easier to interpret by reducing the number of features that explain the classification of health status versus the cost in accuracy. We performed an analysis on a large set of classification techniques and performance metrics, demonstrating that it is possible to construct explainable and reliable models that provide high quality predictive performance.

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This paper presents a new technique to improve the performance of OFDM systems with nonlinear amplifiers. This method aims to achieve good balances between PAPR and BER at low computer cost. A performance and complexity comparison with known techniques for BER reduction is presented. The results show that the proposed method has lower computational complexity (about 33% fewer multiplications), similar BER performance and better PAPR performance than the other techniques here evaluated.

Resumo-Neste trabalho investigamos o emprego de OFDM pré-codificado em canais de HF de faixa larga (HFL). Implementamos um simulador de canais baseado na especificação técnica DRM. Avaliamos o emprego de três técnicas de précodificação em canais HFL, e constatamos ganhos de até 9 dB na razão sinal ruído em 10 −4 de taxa de erro, em relação ao OFDM convencional. Além disso, observamos uma redução de aproximadamente 5,5 dB no valor da PAPR queé superado com probabilidade 10 −3 quando empregamos modulação 16-QAM nas subportadoras. A redução com modulação QPSK foi de 8,5 dB. Palavras-Chave-OFDM, pré-codificação, canais HF de faixa larga (HFL), taxa de erro, PAPR.

Learning systems have been very focused on creating models that are capable of obtaining the best results in error metrics. Recently, the focus has shifted to improvement in order to interpret and explain their results. The need for interpretation is greater when these models are used to support decision making. In some areas this becomes an indispensable requirement, such as in medicine. This paper focuses on the prediction of cardiovascular disease by analyzing the well-known Statlog (Heart) Data Set from the UCI’s Automated Learning Repository. This study will analyze the cost of making predictions easier to interpret by reducing the number of features that explain the classification of health status versus the cost in accuracy. It will be analyzed on a large set of classification techniques and performance metrics. Demonstrating that it is possible to make explainable and reliable models that have a good commitment to predictive performance.