Very Fast Decision Tree (VFDT) algorithm on Hadoop

2016 ◽  
Author(s):  
Sharmishta Desai ◽  
Sourav Roy ◽  
Brina Patel ◽  
Samruddhi Purandare ◽  
Minal Kucheria
Keyword(s):  
Decision Tree ◽  
Very Fast Decision Tree ◽  
Fast Decision

scholarly journals Energy-aware very fast decision tree

2021 ◽  
Author(s):  
Eva García-Martín ◽  
Niklas Lavesson ◽  
Håkan Grahn ◽  
Emiliano Casalicchio ◽  
Veselka Boeva
Keyword(s):  
Energy Consumption ◽  
Decision Tree ◽  
Concept Drift ◽  
Algorithm Design ◽  
Energy Aware ◽  
Battery Capacity ◽  
Very Fast Decision Tree ◽  
Public Datasets ◽  
Additional Constraints ◽  
Fast Decision

AbstractRecently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.

scholarly journals Strict Very Fast Decision Tree: A memory conservative algorithm for data stream mining

2018 ◽  
Vol 116 ◽  
pp. 22-28 ◽  
Author(s):  
Victor Guilherme Turrisi da Costa ◽  
André Carlos Ponce de Leon Ferreira de Carvalho ◽  
Sylvio Barbon Junior
Keyword(s):  
Decision Tree ◽  
Data Stream ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Very Fast Decision Tree ◽  
Fast Decision

scholarly journals Speeding up Very Fast Decision Tree with Low Computational Cost

2020 ◽  
Author(s):  
Jian Sun ◽  
Hongyu Jia ◽  
Bo Hu ◽  
Xiao Huang ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Computational Cost ◽  
Incremental Algorithm ◽  
Whole Process ◽  
High Classification Accuracy ◽  
Very Fast Decision Tree ◽  
Low Computational Cost ◽  
Fast Decision

Very Fast Decision Tree (VFDT) is one of the most widely used online decision tree induction algorithms, and it provides high classification accuracy with theoretical guarantees. In VFDT, the split-attempt operation is essential for leaf-split. It is computation-intensive since it computes the heuristic measure of all attributes of a leaf. To reduce split-attempts, VFDT tries to split at constant intervals (for example, every 200 examples). However, this mechanism introduces split-delay for split can only happen at fixed intervals, which slows down the growth of VFDT and finally lowers accuracy. To address this problem, we first devise an online incremental algorithm that computes the heuristic measure of an attribute with a much lower computational cost. Then a subset of attributes is carefully selected to find a potential split timing using this algorithm. A split-attempt will be carried out once the timing is verified. By the whole process, computational cost and split-delay are lowered significantly. Comprehensive experiments are conducted using multiple synthetic and real datasets. Compared with state-of-the-art algorithms, our method reduces split-attempts by about 5 to 10 times on average with much lower split-delay, which makes our algorithm run faster and more accurate.

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