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Tree-based ensemble algorithms (TEAs) have had a transformative impact in various fields. However, when they are applied to real-time critical problems such as medical analysis, existing TEAs fall short of two-fold issues. The first is the trade-off among model interpretability, accuracy, and explainability. Second, traditional TEAs perform dramatically worse when the number of contributing features to model accuracy is small in comparison to the total number of features. We address the aforementioned issues in this paper and propose a novel "eXplainable Reasonably Randomised Forest" (XRRF) algorithm. The XRRF consists of four conclusive steps: learning performance, intrinsic interpretability, model accuracy, and explainability. The proposed forest algorithm is evaluated on three real-world problems (medical analysis, business analysis, and employee churn), a hybrid artificial dataset, and twenty multidisciplinary benchmark problems with varying characteristics. The experimental results demonstrate that the XRRF outperforms the six mainstream and two cutting-edge black-box and white-box algorithms, respectively. [ABSTRACT FROM AUTHOR]

The algorithm of C4.5 decision tree has the advantages of high classification accuracy, fast calculation speed and comprehensible classification rules, so it is widely used for medical data analysis. However, for imbalanced medical data, the classification accuracy of decision trees-based models is not ideal. Therefore, this paper proposes a cluster-based oversampling algorithm (KNSMOTE) combining Synthetic minority oversampling technique (SMOTE) and k -means algorithm. The sample classes clustered by k -means and the original sample classes are calculated to select the "safe samples" whose sample classes have not been changed. The "safe samples" are linearly interpolated to synthesize the new samples. The improved SMOTE sets the oversampling ratio according to the imbalance ratio of the original samples, which is used to synthesize the samples whose number is the same as that of the original samples. Compared with other oversampling algorithms on 8 UCI datasets, our algorithm has achieved significant advantages. Our algorithm was applied to the medical datasets, and the average values of the Sensitivity and Specificity indexes of the Random forest (RF) algorithm were 99.84% and 99.56%, respectively. [ABSTRACT FROM AUTHOR]

The foremost requirement for a decision forest to achieve better ensemble accuracy is building a set of accurate and diverse individual decision trees as base classifiers. Existing decision forest algorithms mainly differ from each other on how they induce diversity among the decision trees. At the same time, most of the drawbacks of existing algorithms originate from their induction processes of diversity. In this paper, we propose a new decision forest algorithm that is more balanced through effective synchronization between different sources of diversity. The proposed algorithm is balanced theoretically and empirically. We carried out experiments on 25 well-known data sets that are publicly available from the UCI Machine Learning Repository, to perform an extensive empirical evaluation. The experimental results indicate that the proposed algorithm has the best average ensemble accuracy rank of 1.8 compared to its closest competitor at 3.5. Using the Friedman and Bonferroni-Dunn tests, we also show that such an improvement is indeed statistically significant. In addition, the proposed algorithm is found to be competitive in terms of complexity and other relevant parameters. [ABSTRACT FROM AUTHOR]