1 / 21

210 likes | 291 Views

ENTROPY-BASED CONCEPT SHIFT DETECTION PETER VORBURGER, ABRAHAM BERNSTEIN IEEE ICDM 2006. 1. Speaker: Li HueiJyun Advisor: Koh JiaLing Date:2007/11/6. OUTLINE. Introduction Entropy and Concept Shift Adaption Calculating Entropy on Data Streams

Download Presentation
## ENTROPY-BASED CONCEPT SHIFT DETECTION PETER VORBURGER, ABRAHAM BERNSTEIN IEEE ICDM 2006

**An Image/Link below is provided (as is) to download presentation**
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.
Content is provided to you AS IS for your information and personal use only.
Download presentation by click this link.
While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

**ENTROPY-BASED CONCEPT SHIFT DETECTIONPETER VORBURGER,**ABRAHAM BERNSTEINIEEE ICDM 2006 1 Speaker: Li HueiJyun Advisor: Koh JiaLing Date:2007/11/6**OUTLINE**• Introduction • Entropy and Concept Shift Adaption • Calculating Entropy on Data Streams • Algorithm Control Strategy using Entropy Measure • Experimental setup • Experimental Results • Discussion of the Experiments • Application to a Real-World Problem: Context Switches in Sensor Data • Limitations, Future Work, and Conclusion 2**INTRODUCTION**• Problem: • In many applications data is gathered over time, which raises the problem that the concepts to be learned may drift (i.e., change) over time. • The increasing amount of data (e.g., multimedia content, data warehouse ) and limitation of computing power due to miniaturization (e.g., wearable computing) call for faster and more resource friendly algorithms. • Motivation: the analysis of sensor data on wearable devices. 3**INTRODUCTION**• Context-awareness: A Scenario-based Approach for Direct Interruptablity Prediction on Wearable Devices • Classifiers predict peoples’ anticipated behavior based on sensory input • Contexts (or contextual situations) switch rather than gradually change • Contextual information could be reused, even for new, not yet encountered situations • An ongoing monitoring of the sensor stream is needed 4**INTRODUCTION**• Problem: • online pattern matching mechanism comparing the sensor stream to the entire library of already known contexts is computational complex and not yet suitable for today’s wearable devices. • Solution: • indicate possible candidates (or hot spots) for context changes limiting the computationally intensive context (re-)determination on those candidates. 5**ENTROPY AND CONCEPT SHIFT ADAPTION**• Assumptions: • As long as the distribution of older instances (features and target values) is similar to the distribution of new instances no concept drift occurred • A distribution difference between older and more recent instances indicates a change in the target concept • Measure the distribution inequality: • If two distributions are equal, the entropy measure results in a value of 1 • If they are absolutely different the measure will result in a value of 0 6**CALCULATING ENTROPY ON DATA STREAMS**• Sliding window technique: compares two windows, one presenting older and the other representing more recent instances in the stream • Compare the two windows by counting and comparing all instances with respect to their class and stream membership • Discretize the range of instance values to a fixed number of bins to take the approximate value distribution into account 7**CALCULATING ENTROPY ON DATA STREAMS**• A data stream: a sequence consisting of sequentially ordered tuples in time ti • i (1, 2, 3, …) • := ( , li) • where is the vector of all feature stream instances sni at time ti • The domain of the label stream l is discrete and contains all class values c C 8**CALCULATING ENTROPY ON DATA STREAMS**Hi: the resulting entropy at time ti and is defined as the mean of all data stream entropies His at time ti where S: the number of feature-streams His is calculated from the entropies Hiscb**CALCULATING ENTROPY ON DATA STREAMS**Hiscb: represent the entropy of each class (c C) and bin (b B) given the stream s at time ti Bins: discrete aggregation of the values of each feature stream s : the probability that an instance occurs in the old window at time ti, belong to class c, with feature domain of stream s in bin b wiscb: depend on i, s, c, b**ALGORITHM CONTROL STRATEGY USING ENTROPY MEASURE**• Instance selection style algorithm**EXPERIMENTAL SETUP**• Real concept drifts: changes in the actual target concepts • Virtual concept drifts: changes in the distribution • Generate synthetic data set: • H. Wang, W. Fan, P. S. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In KDD ‘03: proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining • Real drift data set • Virtual drift data set • Mixed data set**EXPERIMENTAL SETUP**• Performance measure: • Accuracy • The area under the ROC-curve • A representative set of benchmarks: • Perfect benchmark: assumes an oracle-given ideal window size ξ for any point in time • A selection of ensemble classifiers: the literature so far showed to have the highest accuracy and robustness against noise • P. Vorburger and A. Bernstein. Entropy-based detection of real and virtual concept shifts. Working Paper – University of Zurich, Department of Informatics, 2006**EXPERIMENTAL RESULTS**• The prediction quality against increasing noise levels**EXPERIMENTAL RESULTS**• Computational complexity • Compare ensemble classifiers and the entropy measure based algorithm • Measure the elapsed time: • three committee classifiers: 2031.6±15s • Entropy based algorithm: 148.6s • Entropy calculation without Naïve Bayes model building: 1.1±0.1s**DISCUSSION OF THE EXPERIMENT**• Entropy measure outperforms the ensemble benchmark algorithm on real concept shifts • Exhibit a greater predictive power while requiring less computational resources • The entropy measure based algorithm showed the nearly the same robustness towards noise as the perfect benchmark and the committee classifiers**APPLICATION TO A REAL-WORLD PROBLEM: CONTEXT SWITCHES IN**SENSOR DATA • Data set: • Audio: decomposed into 10 features • accelerometer data recorded over a time of 15381s: merged in one single feature • The wearable data acquisition set up: a microphone and three three-dimentional accelerometers attached on the subject’s shoulder, wrist, and leg**APPLICATION TO A REAL-WORLD PROBLEM: CONTEXT SWITCHES IN**SENSOR DATA • (A)walking (B)streetcar (C)office work • (D)lecture (E)cafeteria (F)meeting**LIMITATIONS, FUTURE WORK, AND CONCLUSION**• Gradual concept drifts • Find boundary conditions • Recognize recurring concepts and exploit this information • Generalizability • The choice of the suitable parameters could be optimized**LIMITATIONS, FUTURE WORK, AND CONCLUSION**• Find a measure for detecting and measuring concept shifts as an analogue for context switches • Formulation of entropy on data streams is capable to detect and measure concept shifts • Algorithm with an entropy based instance selection strategy outperformed ensemble based algorithms on real concept shift data sets • Given algorithm robustness towards noise, its sensitivity towards concept shifts, its computational efficiency, and predictive power on real concept shift data sets

More Related