3 accepted papers
Decision trees are widely used in machine learning for their interpretability and effectiveness in classification tasks. Traditional axis-parallel decision trees partition data using single-feature thresholds at each node, but they often struggle to represent complex, non-axis-aligned decision bound
The Boolean Matrix Factorization (BMF) problem aims to represent a n×m Boolean matrix as the Boolean product of two matrices of small rank k, where the product is computed using Boolean algebra operations. However, finding a BMF of minimum rank is known to be NP-hard, posing challenges for heuristic…
The k-undercover Boolean matrix factorization problem aims to approximate a m×n Boolean matrix X as the Boolean product of an m×k and a k×n matrices A◦B such that X is a cover of A◦B, i.e., no representation error is allowed on the 0’s entries of the matrix X. To infer an optimal and “block-optimal”…