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Identifying Competence-Critical Instances for Instance-Based Learners 2001. 5. 9 Presenter: Kyu-Baek Hwang.

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Presentation on theme: "Identifying Competence-Critical Instances for Instance-Based Learners 2001. 5. 9 Presenter: Kyu-Baek Hwang."— Presentation transcript:

1 Identifying Competence-Critical Instances for Instance-Based Learners 2001. 5. 9 Presenter: Kyu-Baek Hwang

2 Abstract The basic nearest neighbor classifier with a large dataset Classification accuracy and response time Review on past works tackling these problems No consistent method Insight into the problem characteristics Iterative case filtering (ICF) algorithm

3 Introduction Harmful and superfluous instances are stored. Selectively store instances (or delete stored instances) The data miner have to gain an insight into the structure of the classes in the instance space. The experimental comparison of RT3 and ICF Neither algorithm performs better in all cases.

4 Defining the Problem Two practical issues that arise in this area  Instance removal (retain only the critical instances)  Different approaches according to the type of the classification problem The same (or higher) accuracy and the less storage Which instance should be deleted?

5 Four Cases Where NNC Fails Noisy instance Close to the interclass border  Border instances are critical in general. Small region defining the class  Small k values cope with this kind of problem. Unsolvable problem

6 Instance Space Structure Two categories of instance space structure  Homogeneous region (locality)  Non-homogeneous region (no locality)

7 Which Instances Are Critical? Prototypes  For non-homogeneous regions Instances with high utility  Needs classification feedback Instances which lie on borders are almost always critical.

8 Review Competence enhancement  By removing noisy or corrupt instances Competence preservation  By removing superfluous instances Hybrid approach  Many modern approaches

9 Competence Enhancement Stochastic noise Wilson Editing  All instances which are incorrectly classified by their nearest neighbors are assumed to be nosy instances.  Smoothing effect  Empirically tested Noisy instances and genuine exceptions

10 Competence Preservation Condensed nearest neighbor (CNN)  Look for cases for which removal does not lead to additional miss- classification Chang’s algorithm (Korean)  Merging two instances into one synthetic instance (the prototype) Footprint deletion policy  Local-set of a case c  The set of cases contained in the largest hypersphere centered on c such that only cases in the same class as c are contained in the hypersphere.

11 Footprint Deletion Policy For a case-base CB = {c 1, c 2, …, c n }  Coverage(c) = {c’  CB: c’  Local-set(c)}  Reachable(c) = {c’  CB: c  Local-set(c’)} Pivotal group  With an empty reachable set Delete the instance with large local-set

12 Hybrid Approaches (1/2) IB2 (on-line)  If a new case to be added can already be classified correctly on the basis of the current case-base, the case is discarded. IB3  IB2 with time delay The order of presentation is crucial for IB2 and IB3. RT1  k nearest neighbor  Associates of the case p are the cases that have p as their k nearest neighbor.  The instance which has many associates is tested and removed.

13 Hybrid Approaches (2/2) RT2 is identical to RT1 and additionally,  Cases furthest from their nearest enemy are removed first.  Removed associates still guide the deletion process. RT3 is identical to RT2 and additionally,  Wilson’s noise filtering step is executed first. RT algorithms are analogous to the footprint deletion policy.

14 An Iterative Case Filtering Algorithm Coverage set and reachable set RTn algorithm  Associate set of fixed size Remove cases which have a reachable set size greater than the coverage set size.  Intuitively, this approach removes the cases that are far from the border. A noisy case will have a singleton reachable set and a singleton coverage set.  This property protects the noisy case from being removed.  Wilson Editing

15 ICF Algorithm

16 How The ICF Algorithm Proceeds?

17 Experiments Experiments on 30 datasets from UCI repository Maximum number of iterations: 17  switzerland database In general, 3 iterations are required.

18 Reduction Profiles The percentage of cases removed after each iteration  switzerland database: 17 iterations, 2 – 13% (complicated)  zoo database: 2 iterations, 37% (simple structure)

19 Comparative Evaluation (1) Early approaches  CNN, RNN, SNN, Chang, Wilson Editing, repeated Wilson Editing, and all k-NN (2) Recent editions  IB2, IB3, TIBLE, and IBL-MDL (3) State of the art  RT3 and ICF

20 RT3 and ICF

21 Conclusions The structure of the instance space is important. ICF and RT3 behave in very similar way.  The intrinsic properties of them are similar.  80% of removal and the little degradation of accuracy. The reduction profile provides some insights into the property of the problem.

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