We introduce a new form of game search called partition search that incorporates dependency analysis, allowing substantial reductions in the portion of the tree that needs to be expanded. Both theoretical results and experimental data are presented. For the game of bridge, partition search provides approximately as much of an improvement over existing methods as a-0 pruning provides over minimax.

Prism is a modular classification rule generation method based on the ‘separate and conquer’ approach that is alternative to the rule induction approach using decision trees also known as ‘divide and conquer’. Prism often achieves a similar level of classification accuracy compared with decision trees, but tends to produce a more compact noise tolerant set of classification rules. As with other...

Progressive Pruning (PP) is used in the Monte-Carlo go playing program Indigo. For each candidate move, PP launches random games starting with this move. PP gathers statistics on moves, and it prunes moves statistically inferior to the best one [5]. This papers yields two new pruning techniques: Miai Pruning (MP) and Set Pruning (SP). In MP the second move of the random games is selected at ran...

Effective pruning is essential for efficient iceberg cube computation. Previous studies have focused on exclusive pruning: regions of a search space that do not satisfy some condition are excluded from computation. In this paper we propose inclusive and anti-pruning. With inclusive pruning, necessary conditions that solutions must satisfy are identified and regions that can not be reached by su...

Dots-And-Boxes is a well-known and widely-played combinatorial game. While the rules of play are very simple, the state space for even very small games is extremely large, and finding the outcome under optimal play is correspondingly hard. In this paper we introduce a Dots-And-Boxes solver which is significantly faster than the current state-of-the-art: over an order-of-magnitude faster on seve...

In this work we investigate several issues in order to improve the performance of probabilistic estimation trees (PETs). First, we derive a new probability smoothing that takes into account the class distributions of all the nodes from the root to each leaf. Secondly, we introduce or adapt some new splitting criteria aimed at improving probability estimates rather than improving classification ...

Internet Traffic classification is vital for various network activities such as detection of malware. Security is major issue of concern which accounts for the reputation and reliability of system. Malware effects the system adversely results in data loss or abnormal functioning. Hence, detection and as well as removal of malware is crucial. Combining set of classifiers called as ensembling pro...

We describe an experimental study of pruning methods for decision tree classiiers when the goal is minimizing loss rather than error. In addition to two common methods for error minimization, CART's cost-complexity pruning and C4.5's error-based pruning, we study the extension of cost-complexity pruning to loss and one pruning variant based on the Laplace correction. We perform an empirical com...