We present an analytic evaluation of the run-time behavior of the C4.5 algorithm which highlights some efficiency improvements. We have implemented a more efficient version of the algorithm, called EC4.5, that improves on C4.5 by adopting the best among three strategies at each node construction. The first strategy uses a binary search of thresholds instead of the linear search of C4.5. The second strategy adopts a counting sort method instead of the quicksort of C4.5. The third strategy uses a main-memory version of the RainForest algorithm for constructing decision trees. Our implementation computes the same decision trees as C4.5 with a performance gain of up to 5 times
The current availability of efficient algorithms for deci- sion tree induction makes intricate post-...
This paper takes a new look at two sampling schemes commonly used to adapt machine algorithms to imb...
We describe five heuristic techniques to optimize decision trees of uniform depth, that is, to minim...
We present an analytic evaluation of the runtime behavior of the C4.5 algorithm which highlights som...
In machine learning, decision trees are employed extensively in solving classification problems. In ...
This paper presents a new algorithm to improve the speed of threshold searching process in C4.5 by u...
Abstract. Selecting the close-to-optimal collective algorithm based on the parameters of the collect...
In this report, we study a problem and design an efficient algorithm to solve the problem. We implem...
The whole computer hardware industry embraced multicores. For these machines, the extreme optimisati...
Within this paper we advise a distributed implementation of C4.5 formula using MapReduce computing m...
This paper describes a new admissible tree search algorithm called Iterative Threshold Search (ITS)....
this paper is to push this interaction further in light of these recent developments. In particular,...
This paper describes a new admissible tree search algorithm called Iterative Threshold Search (ITS)....
The whole computer hardware industry embraced multicores. For these machines, the extreme optimisati...
The whole computer hardware industry embraced the multi-core. The extreme optimisation of sequential...
The current availability of efficient algorithms for deci- sion tree induction makes intricate post-...
This paper takes a new look at two sampling schemes commonly used to adapt machine algorithms to imb...
We describe five heuristic techniques to optimize decision trees of uniform depth, that is, to minim...
We present an analytic evaluation of the runtime behavior of the C4.5 algorithm which highlights som...
In machine learning, decision trees are employed extensively in solving classification problems. In ...
This paper presents a new algorithm to improve the speed of threshold searching process in C4.5 by u...
Abstract. Selecting the close-to-optimal collective algorithm based on the parameters of the collect...
In this report, we study a problem and design an efficient algorithm to solve the problem. We implem...
The whole computer hardware industry embraced multicores. For these machines, the extreme optimisati...
Within this paper we advise a distributed implementation of C4.5 formula using MapReduce computing m...
This paper describes a new admissible tree search algorithm called Iterative Threshold Search (ITS)....
this paper is to push this interaction further in light of these recent developments. In particular,...
This paper describes a new admissible tree search algorithm called Iterative Threshold Search (ITS)....
The whole computer hardware industry embraced multicores. For these machines, the extreme optimisati...
The whole computer hardware industry embraced the multi-core. The extreme optimisation of sequential...
The current availability of efficient algorithms for deci- sion tree induction makes intricate post-...
This paper takes a new look at two sampling schemes commonly used to adapt machine algorithms to imb...
We describe five heuristic techniques to optimize decision trees of uniform depth, that is, to minim...