Knowledge discovery in data streams with regression tree methods

Alberg, Dima; Kandel, Abraham

doi:10.1002/widm.51

Cited by 35 publications

(18 citation statements)

References 42 publications

(58 reference statements)

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“…Finally, a recent article [Moreno-Torres et al 2012] focuses on describing various ways how data distribution can change over time and only briefly covers adaptation techniques from dataset shift community perspective, mostly leaving out works on concept drift. A recent review [Alberg et al 2012] focuses on decision trees.…”

Section: Introductionmentioning

confidence: 99%

A survey on concept drift adaptation

et al. 2014

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Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the concept drift adaptation presents the state of the art techniques and a collection of benchmarks for researchers, industry analysts and practitioners. The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art.

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Section: Introductionmentioning

confidence: 99%

A survey on concept drift adaptation

et al. 2014

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“…Most batch regression and tree models for predicting numerical variables, such as MARS [7], CART [3], RETIS [10], M5 [12], M5P [14], SMOTI [5], MAUVE [13], MOPT [1], GUIDE [11], and FIMT [9], are not designed for aggregated temporal data. Hence, they cannot utilize the relationship between multiple statistical moments (such as mean and standard deviation) of aggregated numerical attributes.…”

Section: Related Workmentioning

confidence: 99%

“…However, according to Breiman et al [4], these methods are known for their split instability. Finally, the interested reader may find a more detailed survey of regression tree methods in [1].…”

Section: Related Workmentioning

confidence: 99%

An Interval Tree Approach to Predict Forest Fires using Meteorological Data

Alberg¹

2015

IJCA

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Interval prediction can be more useful than single value prediction in many continuous data streams. This paper introduces a novel Interval Prediction Tree IP3 algorithm for interval prediction of numerical target variables from temporal mean-variance aggregated continuous data. This algorithm characterized by: processing incoming meanvariance aggregated multivariate temporal data, splitting each of the continuous features of the input according to the best mean-variance and making stable interval predictions of a target numerical variable with a given degree of statistical confidence. As shown by empirical evaluations in forest fires data set the proposed method provides better performance than existing regression tree models.

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“…Incremental approaches, on the other hand, face the problem of dealing with continuous, and potentially computationally expensive, updates to the regression tree model. Recently, the hybrid approach of batch-incremental model building, where the model is updated using batches of new instances, typically using a sliding window, has become the most common approach [13].…”

Section: Related Workmentioning

confidence: 99%

Regression trees for streaming data with local performance guarantees

Johansson

Sönströd

Linusson

et al. 2014

2014 IEEE International Conference on Big Data (Big Data)

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Abstract-Online predictive modeling of streaming data is a key task for big data analytics. In this paper, a novel approach for efficient online learning of regression trees is proposed, which continuously updates, rather than retrains, the tree as more labeled data become available. A conformal predictor outputs prediction sets instead of point predictions; which for regression translates into prediction intervals. The key property of a conformal predictor is that it is always valid, i.e., the error rate, on novel data, is bounded by a preset significance level. Here, we suggest applying Mondrian conformal prediction on top of the resulting models, in order to obtain regression trees where not only the tree, but also each and every rule, corresponding to a path from the root node to a leaf, is valid. Using Mondrian conformal prediction, it becomes possible to analyze and explore the different rules separately, knowing that their accuracy, in the long run, will not be below the preset significance level. An empirical investigation, using 17 publicly available data sets, confirms that the resulting rules are independently valid, but also shows that the prediction intervals are smaller, on average, than when only the global model is required to be valid. All-in-all, the suggested method provides a data miner or a decision maker with highly informative predictive models of streaming data.

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Knowledge discovery in data streams with regression tree methods

Cited by 35 publications

References 42 publications

A survey on concept drift adaptation

A survey on concept drift adaptation

An Interval Tree Approach to Predict Forest Fires using Meteorological Data

Regression trees for streaming data with local performance guarantees

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