<ul><li>Decision trees are crucial in machine learning for data representation and classification.</li><li>Efforts are being made to create compact decision trees by minimizing their size or depth.</li><li>The parameterized complexity of Decision Tree Learning is currently a topic of significant interest.</li><li>Two problems, DTSO and DTDO, focus on constructing decision trees with minimal size and depth, respectively.</li></ul>

Learning Small Decision Trees with Few Outliers: A Parameterized Perspective

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