Algorithm 1 The MOTiFS Algorithm

Load dataset and preprocess Initialize SCALAR, BUDGET //Scaling factor & Number of MCTS simulations (hyper parameters) function MOTiFS (featuresList)   create rootNode   maxReward, bestFeatureSubset ← UCTSEARCH (rootNode)   return (maxReward, bestFeatureSubset) function UCTSEARCH (rootNode)   Initialize maxReward, bestFeatureSubset   while within computational budget do    frontNode ← TREEPOLICY (rootNode)    reward, featureSubset ← DEFAULTPOLICY (frontNode.state)    BACKUP (frontNode, reward)    if reward is greater than maxReward then     maxReward ← reward     bestFeatureSubset ← featureSubset   return (maxReward, bestFeatureSubset) function TREEPOLICY (node)   while node is non-terminal do    if node not fully expanded then     return EXPAND (node)    else     node ← BESTCHILD (node, SCALAR)   return node function EXPAND (node)   choose a $\in$ untried actions from A(node.state)   add a newChild with f(node.state, a)   return newChild function BESTCHILD ($v$, $C$)   return $\underset{v^{\prime }\in \mathrm{children}\mathrm{of} v}{\max }\max \left(Q_{v^{\prime }}\right)+C\sqrt{\frac{2 \times ln\left(v.visits\right)}{v^{\prime }.visits}}$ function DEFAULTPOLICY (state)   while state is non-terminal do    choose a $\in$ A(state) uniformly at random   state ← f(state, a) traverse state.path    if ai is equal to fi+1 then     featureSubset ← INCLUDE (fi+1)    reward ← REWARD (featureSubset)    return (reward, featureSubset) function BACKUP (node, reward)   while node is not null do    node.visits ← node.visits + 1    if reward > node.reward then     node.reward ← reward    node ← node.parent   return