In this paper we show that the move pruning method we presented at SoCS last year sometimes prunes all the least-cost paths from one state to another. We present two examples exhibiting this erroneous behaviour–a simple, artificial example and a slightly more complex example that arose in last year’s experiments. We then formally prove that a simple modification to our move pruning method makes...

There are many methods to prune decision trees, but the idea of cost-sensitive pruning has received much less investigation even though additional flexibility and increased performance can be obtained from this method. In this paper, we introduce a cost-sensitive decision tree pruning algorithm called CC4.5 based on the C4.5 algorithm. This algorithm uses the same method as C4.5 to construct th...

Several methods have been proposed in the literature for decision tree (post)-pruning. This article presents a unifying framework according to which any pruning method can be de"ned as a four-tuple (Space, Operators, Evaluation function, Search strategy), and the pruning process can be cast as an optimization problem. Six well-known pruning methods are investigated by means of this framework an...

Forward pruning, also known as selective search, is now employed in many strong game-playing programs. In this paper, we introduce RankCut – a domain independent forward pruning technique which makes use of move ordering, and prunes once no better move is likely to be available. Since gameplaying programs already perform move ordering to improve the performance of αβ search, this information is...

Pruning is a method for reducing the error and complexity of induced trees. There are several approaches to pruning decision trees, while regression trees have attracted less attention. We propose a method for pruning regression trees based on the sound foundations of the MDL principle. We develop coding schemes for various constructs and models in the leaves and empirically test the new method...

| We address pruning and evaluation of Tapped-Delay Neural Networks for the sunspot benchmark series. It is shown that the generalization ability of the networks can be improved by pruning using the Optimal Brain Damage method of Le Cun, Denker and Solla. A stop criterion for the pruning algorithm is formulated using a modiied version of Akaike's Final Prediction Error estimate. With the propos...

In this paper, we proposed a new sensitivity-based adaptive architecture pruning algorithm for Madalines. The algorithm establishes a pruning measure based on the network sensitivity to its structure variation and a minimal disturbance principle. The measure can be used to evaluate the performance loss due to its structure changes more or less. And the loss can be compensated by relearning. Thu...

The cost-complexity pruning generates nested subtrees and selects the best one. However, its computational cost is large since it uses hold-out sample or crossvalidation. On the other hand, the pruning algorithms based on posterior calculations such as BIC (MDL) and MEP are faster, but they sometimes produce too big or small trees to yield poor generalization errors. In this paper, we propose a...

Developmental axon pruning is widely used to refine neural circuits. We performed a mosaic screen to identify mutations affecting axon pruning of Drosophila mushroom body gamma neurons. We constructed a modified piggyBac vector with improved mutagenicity and generated insertions in >2000 genes. We identified two cohesin subunits (SMC1 and SA) as being essential for axon pruning. The cohesin com...

In this paper, we propose a Runtime Neural Pruning (RNP) framework which prunes the deep neural network dynamically at the runtime. Unlike existing neural pruning methods which produce a fixed pruned model for deployment, our method preserves the full ability of the original network and conducts pruning according to the input image and current feature maps adaptively. The pruning is performed i...