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Laplacian-Weighted Random Forest for High-Dimensional Data Classification

Random forest has been an important technique in ensemble classification, due to its effectiveness and robustness in handling complex data. But many of the previous random forest models tend to treat all features equally and often lack the ability to well reflect the potentially different importance of different features, especially in high-dimensional feature space. To tackle this issue, this paper proposes a novel approach termed Laplacian-weighted random forest (LWRF). Particularly, we first build a k-nearest neighbor graph to depict the underlying structure of the data. Then, we take advantage of the Laplacian score to estimate the importance of different features by considering their locality preserving power, based on which an efficient feature weighting scheme can be devised. With the feature weights via Laplacian scores, we proceed to generate a set of diverse subspaces by weighted random sampling, and thereafter present the LWRF approach with the decision trees trained on these diverse subspaces. Experiments have been conducted on twelve high-dimensional gene expression datasets to demonstrate the effectiveness and efficiency of our approach.