Query: Improved random forest

title: An improved random forest based rule extraction method for breast cancer diagnosis
abstract: Breast cancer has been becoming the main cause of death in women all around the world. An accurate and interpretable method is necessary for diagnosing patients with breast cancer for well performed treatment. Nowadays, a great many of ensemble methods have been widely applied to breast cancer diagnosis, capable of achieving high accuracy, such as Random Forest. However, they are black box methods which are unable to explain the reasons behind the diagnosis. To surmount this limitation, a rule extraction method named improved Random Forest (RF) based rule extraction (IRFRE) method is developed to derive accurate and interpretable classification rules from a decision tree ensemble for breast cancer diagnosis. Firstly, numbers of decision tree models are constructed using Random Forest to generate abundant decision rules available. And then a rule extraction approach is devised to detach decision rules from the trained trees. Finally, an improved multi objective evolutionary algorithm (MOEA) is employed to seek for an optimal rule predictor where the constituent rule set is the best trade off between accuracy and interpretability. The developed method is evaluated on three breast cancer data sets, i.e. the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, Wisconsin Original Breast Cancer (WOBC) dataset, and Surveillance, Epidemiology and End Results (SEER) breast cancer dataset. The experimental results demonstrate that the developed method can primely explain the black box methods and outperform several popular single algorithms, ensemble learning methods, and rule extraction methods from the view of accuracy and interpretability. What is more, the proposed method can be popularized to other cancer diagnoses in practice, which provides an option to a more interpretable, more accurate cancer diagnosis process.

title: FSRF:An Improved Random Forest for Classification
abstract: Random forest algorithm is a flexible and easy to use machine learning algorithm, which is widely used in classification problems. However, the traditional random forest has some limitations. Because the randomness added by random forest to decision trees almost only occurs in the feature selection when the decision trees are generated, the fixity of decision trees generation rules will lead to relatively serious over fitting. At the same time, in the face of data with high and unbalanced feature dimensions, the performance of algorithm is seriously weakened because high dimensional data usually contains many irrelevant and redundant features. To solve these problems, we propose an improved random forest algorithm FSRF. Based on the traditional random forest algorithm, we use the feature selection methods to preprocess the data and get the feature subset with the best classification performance to construct the random forest. At the same time, we introduce sparse matrix projection to improve the generation of the random forest. Experiments show that our method reduces the influence of redundant features on classification and improves the accuracy.

title: An Improved Random Forest Algorithm for Predicting Employee Turnover
abstract: Employee turnover is considered a major problem for many organizations and enterprises. The problem is critical because it affects not only the sustainability of work but also the continuity of enterprise planning and culture. Therefore, human resource departments are paying greater attention to employee turnover seeking to improve their understanding of the underlying reasons and main factors. To address this need, this study aims to enhance the ability to forecast employee turnover and introduce a new method based on an improved random forest algorithm. The proposed weighted quadratic random forest algorithm is applied to employee turnover data with high dimensional unbalanced characteristics. First, the random forest algorithm is used to order feature importance and reduce dimensions. Second, the selected features are used with the random forest algorithm and the F measure values are calculated for each decision tree as weights to build the prediction model for employee turnover. In the area of employee turnover forecasting, compared with the random forest, C4.5, Logistic, BP, and other algorithms, the proposed algorithm shows significant improvement in terms of various performance indicators, specifically recall and F measure. In the experiment using the employee dataset of a branch of a communications company in China, the key factors influencing employee turnover were identified as monthly income, overtime, age, distance from home, years at the company, and percent of salary increase. Among them, monthly income and overtime were the two most important factors. The study offers a new analytic method that can help human resource departments predict employee turnover more accurately and its experimental results provide further insights to reduce employee turnover intention.

title: A generalized method to predict the compressive strength of high performance concrete by improved random forest algorithm
abstract: The prediction results of high performance concrete compressive strength (HPCCS) based on machine learning methods are seriously influenced by input variables and model parameters. This study proposes a method with two stages to select proper variables, simplify parameter settings, and predict HPCCS. The appropriate variables are selected in the first stage by measuring their importance based on random forest, and then are optimized to predict HPCCS in the second stage. The results show that the proposed method was effective for input variable optimization, and could return better predictions than that without variable optimization, provided that the parameters are set within a reasonable range. Compared with previous models, the proposed method shows a strong generalization capacity for HPCCS prediction. We find that the prediction performance of the model is better when the input variables are expressed as absolute mass, and the model performers well when the actual compressive strength of HPC is high.

title: AN IMPROVED RANDOM FOREST APPROACH FOR PREDICTING TUBERCULOSIS

title: An Improved Random Forest Algorithm

title: Improved Random Forest for Classification
abstract: We propose an improved random forest classifier that performs classification with a minimum number of trees. The proposed method iteratively removes some unimportant features. Based on the number of important and unimportant features, we formulate a novel theoretical upper limit on the number of trees to be added to the forest to ensure improvement in classification accuracy. Our algorithm converges with a reduced but important set of features. We prove that further addition of trees or further reduction of features does not improve classification performance. The efficacy of the proposed approach is demonstrated through experiments on benchmark data sets. We further use the proposed classifier to detect mitotic nuclei in the histopathological data sets of breast tissues. We also apply our method on the industrial data set of dual phase steel microstructures to classify different phases. Results of our method on different data sets show significant reduction in an average classification error compared with a number of competing methods.

title: Identifying channel sand body from multiple seismic attributes with an improved random forest algorithm
abstract: Machine learning provides numerous data driven tools for automatic pattern recognition. Even though various algorithms such as neural networks and support vector machines have been widely applied, it is still necessary to explore new paradigms and algorithms to improve the machine learning assisted seismic interpretation. Random Forest (RF) is a widely used ensemble algorithm, however, only limited studies of random forest in the seismic application were published. In this article, the methodology of random forest is introduced systematically. Meanwhile, to solve the problem of hyper parameter determination, we propose an improved algorithm named Pruning Random Forest (PRF) To reveal the advantages of PRF in terms of predictive performance, robustness, and feature selection compared with support vector machine, neural network, and decision tree, several well designed experiments are executed based on the seismic data of the western Bohai Sea of China. The potential and advantages of random forest in the present case are confirmed by various experiments, which substantiates that the proposed pruning random forest algorithm provides a reliable alternative way for further machine learning assisted seismic interpretation.

title: An Improved Random Forest Algorithm Based on Attribute Compatibility
abstract: ID3 algorithm and C4.5 algorithm of decision tree are commonly used in the base classifier in random forest. In order to solve the problem that the basic classifier algorithm is biased to select multi valued attributes and contains a lot of logarithmic operations, the attribute compatibility of rough set is introduced in this paper. Rough set is a mathematical tool for dealing with uncertain information. In this paper, the expression of information gain or information gain rate in the base classifier is reconstructed with compatibility, and the attribute with the highest compatibility is selected as the segmentation attribute to segment the data set. Finally, three UCI data sets are used to verify the accuracy of the algorithm. The experimental results show that the proposed algorithm has a higher accuracy than the traditional random forest algorithm.

title: Short term prediction of groundwater level using improved random forest regression with a combination of random features
abstract: To solve the problem where by the available on site input data are too scarce to predict the level of groundwater, this paper proposes an algorithm to make this prediction called the canonical correlation forest algorithm with a combination of random features. To assess the effectiveness of the proposed algorithm, groundwater levels and meteorological data for the Daguhe River groundwater source field, in Qingdao, China, were used. First, the results of a comparison among three regressors showed that the proposed algorithm is superior in terms of forecasting variations in groundwater level. Second, the results of experiments were used to show the comparative superiority of the proposed method in terms of training time and complexity of parameter optimization. Third, using the proposed algorithm, the highest prediction accuracy was achieved by employing precipitation P(t 2) temperature T(t) and groundwater level H(t) as the best time lag. This improved random forest regression model yielded higher accuracy in forecasting the variation in groundwater level. The proposed algorithm can also be applied to cases involving low dimensional data.