Abstract This study uses detect breast cancer based on Random Forest (RF). It is crucial to diagnose the illness identify treatment solutions closely linked patient safety. Breast diagnosed using past medical records and various classification methods used in data mining fields today. Each technique performs differently depending input feature types model parameters. Neutral Networks have been ...

Random Forest is an ensemble of classification algorithm widely used in much application especially with larger datasets because of its outstanding features like Variable Importance measure, OOB error detection, Proximity among the feature and handling of imbalanceddatasets. This paper discusses many applications which use Random Forest to classify the dataset like Network intrusion detection, ...

This talk has two main parts. The first part will focus on the use of pair-wise meta-rules for algorithm ranking and selection. Such rules can provide interesting insights on their own, but they are also very valuable features for more sophisticated schemes like Random Forests. A hierarchical variant is able to address complexity issues when the number of algorithms to compare is substantial. T...

Major severe weather events can cause a significant loss of life and property. We seek to revolutionize our understanding of and our ability to predict such events through the mining of severe weather data. Because weather is inherently a spatiotemporal phenomenon, mining such data requires a model capable of representing and reasoning about complex spatiotemporal dynamics, including temporally...

We consider the problem of data-stream classification, introducing a stream-classification algorithm, Dynamic Streaming Random Forests, that is able to handle evolving data streams using an entropy-based drift-detection technique. The algorithm automatically adjusts its parameters based on the data seen so far. Experimental results show that the algorithm handles multi-class problems for which ...

Major severe weather events can cause a significant loss of life and property. We seek to revolutionize our understanding of and ability to predict such events through the mining of severe weather data. Because weather is inherently a spatiotemporal phenomenon, mining such data requires a model capable of representing and reasoning about complex spatiotemporal dynamics, including temporally and...

Two algorithms for inserting a random element into a random heap are shown to be optimal (in the sense that they use the least number of comparisons on the average among all comparison-based algorithms) for different values of n under a uniform model.

Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two ways. We present a new theoretically tractable variant of random regression forests and prove that our algorithm is consistent. We also provide an empirical evaluation, comparing our algorithm and other theoretically ...

We analyze the role of first (leading) author gender on the number of citations that a paper receives, on the publishing frequency and on the self-citing tendency. We consider a complete sample of over 200,000 publications from 1950 to 2015 from five major astronomy journals. We determine the gender of the first author for over 70% of all publications. The fraction of papers which have a female...