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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 random forest classifier is widely used in different fields due to its accuracy and robustness. Since its invention, the random forest algorithm is naturally developed for multi-dimensional vectorial data since features can be directly sampled during the decision tree construction procedure. In the context of discrete sequence classification, an explicit feature set is not readily available and we need to employ a feature extraction algorithm before building the random forest. However, such a predefined feature subset may limit the diversity of decision trees since the set of candidate features is composed of all subsequences. As a result, the predictive accuracy of constructed random forest classifier may be reduced. To address this, we propose a new algorithm that is able to directly build a random forest by choosing features from the set of all subsequences adaptively. To improve the running efficiency of our algorithm, the count-suffix tree is utilized to facilitate the fast frequency counting of subsequences so as to accelerate the generation of each randomized decision tree. The experimental results on 15 real datasets show that our method can outperform those state-of-the-art classification algorithms in terms of the predictive accuracy. The source code of our method can be found at: https://github.com/JiaqiWang-dlut/RSForest. [ABSTRACT FROM AUTHOR]

• Adopt clustering to improve the performance of classification algorithms. • Propose a dynamic weighted probability combination strategy. • Apply an improved PSO to optimize multiple key sensitive parameters in CIEL. • Demonstrate CIEL's good performance experimentally on a large number of datasets. Classification is a vital task in machine learning. By learning patterns of samples of known categories, the model can develop the ability to distinguish the categories of samples of unknown categories. Noticing the advantages of the clustering method in cluster structure analysis, we combine the clustering and classification methods to develop the novel cluster-based intelligence ensemble learning (CIEL) method. We use the clustering method to analyze the inherent distribution of the data and divide all the samples into clusters according to the characteristics of the dataset. Then, for each specific cluster, we use different classification algorithms to establish the corresponding classification model. Finally, we integrate the prediction results of each base classifier to form the final prediction result. In view of the problem of parameter sensitivity, we use a swarm intelligence algorithm to optimize the key parameters involved in the clustering, classification, and ensemble stages in order to boost the classification performance. To assess the effectiveness of CIEL, we perform tenfold cross-validation experiments on the 24 benchmark datasets provided by UCI and KEEL. Designed to improve the performance of the classifiers, CIEL outperforms other popular machine learning methods such as naive Bayes, k -nearest neighbors, random forest, and support vector machine. [ABSTRACT FROM AUTHOR]