Publish in OALib Journal
APC: Only $99
In machine learning, selecting useful features and rejecting redundant features is the prerequisite for better modeling and prediction. In this paper, we first study representative feature selection methods based on correlation analysis, and demonstrate that they do not work well for time series though they can work well for static systems. Then, theoretical analysis for linear time series is carried out to show why they fail. Based on these observations, we propose a new correlation-based feature selection method. Our main idea is that the features highly correlated with progressive response while lowly correlated with other features should be selected, and for groups of selected features with similar residuals, the one with a smaller number of features should be selected. For linear and nonlinear time series, the proposed method yields high accuracy in both feature selection and feature rejection.
Object: To Provide a basis for the
government optimizing the allocation of health resources. Methods: The overall
fairness was analyzed by Theil index and Gini
coefficient. The main causes of unfairness were estimated by the decomposability
of Thiel index. Results: Health resources owned by the “One-hour Economic
Circle” were 1.5 - 2 times than that of the “Two wings”. Theil index
and Gini coefficient of five health resources from small to large were sickbed,
doctor, health worker, nurse, and medical equipment. Differences within region in
the contribution rate of the total Theil index were greater than the difference
between region and the tendency to expand. Conclusions: The total amount of the
health resources in Chongqing is insufficient. The configuration of doctor is
more equitable than nurse, while the medical equipment’ fairness is worst.
Differences within region mainly cause the unfairness.
This paper presents new trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock prices, we construct models which relate the market indicators to a trading decision directly. Furthermore, unlike a reversal trading system or a binary system of buy and sell, we allow three modes of trades, namely, buy, sell or stand by, and the stand-by case is important as it caters to the market conditions where a model does not produce a strong signal of buy or sell. Linear trading models are firstly developed with the scoring technique which weights higher on successful indicators, as well as with the Least Squares technique which tries to match the past perfect trades with its weights. The linear models are then made adaptive by using the forgetting factor to address market changes. Because stock markets could be highly nonlinear sometimes, the Random Forest is adopted as a nonlinear trading model, and improved with Gradient Boosting to form a new technique—Gradient Boosted Random Forest. All the models are trained and evaluated on nine stocks and one index, and statistical tests such as randomness, linear and nonlinear correlations are conducted on the data to check the statistical significance of the inputs and their relation with the output before a model is trained. Our empirical results show that the proposed trading methods are able to generate excess returns compared with the buy-and-hold strategy.