Some enhancements of decision tree bagging

in Proceedings of PKDD 2000, 4th European Conference on Principles of Data Mining and Knowledge Discovery (2000)

This paper investigates enhancements of decision tree bagging which mainly aims at improving computation times, but also accuracy. The three questions which are reconsidered are: discretization of ... [more ▼]

This paper investigates enhancements of decision tree bagging which mainly aims at improving computation times, but also accuracy. The three questions which are reconsidered are: discretization of continuous attributes, tree pruning, and sampling schemes. A very simple discretization procedure is proposed, resulting in a dramatic speedup without significant decrease in accuracy. Then a new method is proposed to prune an ensemble of trees in a combined fashion, which is significantly more effective than individual pruning. Finally, different resampling schemes are considered leading to different CPU time/accuracy tradeoffs. Combining all these enhancements makes it possible to apply tree bagging to very large datasets, with computational performances similar to single tree induction. Simulations are carried out on two synthetic databases and four real-life datasets. [less ▲]

Data mining tools and application in power system engineering

in Proceedings of the 13th Power System Computation Conference, PSCC99 (1999)

The power system field is presently facing an explosive growth of data. The data mining (DM) approach provides tools for making explicit some implicit subtle structure in data. Applying data mining to ... [more ▼]

The power system field is presently facing an explosive growth of data. The data mining (DM) approach provides tools for making explicit some implicit subtle structure in data. Applying data mining to power system engineering is an iterative and interactive process, requiring an acquainted user with the application specifics. The paper describes data mining tools like statistical methos, visualization, machine learning and neural networks, exemplifying by results obtained with a DM software developed for dynamic security assessment studies. Power system engineering applications where data mining would be useful are reviewed in the second part of the paper. [less ▲]

Early prediction of electric power system blackouts by temporal machine learning

in Proceedings of ICML-AAAI 98 Workshop on "Predicting the future: AI approaches to time series analysis" (1998)

This paper discusses the application of machine learning to the design of power system blackout prediction criteria, using a large database of random power system scenarios generated by Monte-Carlo ... [more ▼]

This paper discusses the application of machine learning to the design of power system blackout prediction criteria, using a large database of random power system scenarios generated by Monte-Carlo simulation. Each scenario is described by temporal variables and sequences of events describing the dynamics of the system as it might be observed from real-time measurements. The aime is to exploit the data base in order to derive as simple as possible rules which would allow to detect an incipient blackout early enough to prevent or mitigate it. We propose a novel "temporal tree induction" algorithm in order to exploit temporal attributes and reach a compromise between the degree of anticipation and selectivity of detection rules. Tests are carried out on a a data base related to voltage collapse of an existing large scale power system. [less ▲]