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A Classification Data Set for PLM

DESCRIPTION

This dataset focuses on handwritten digit recognition, specifically digits 0 through 9. It involves transforming 32x32 bitmaps into non-overlapping 4x4 blocks, counting the number of on-pixels in each block to create an 8x8 binary matrix. The training set contains 3,823 examples, while the test set includes 1,797 examples. Each entry in the text files consists of 64 binary values followed by a label. The k-NN classification method (k=3) achieved an accuracy of 93.10% on the test set, demonstrating effective classification of handwritten digits.

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A Classification Data Set for PLM

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  1. A Classification Data Set for PLM Information Theory of Learning Sep. 15, 2005 (c) 2000-2005 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr

  2. Introduction to Data (1) • Handwritten digits (0 ~ 9) • From 32x32 bitmaps, non-overlapping 4x4 blocks are extracted. (c) 2000-2005 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr

  3. Introduction to Data (2) • # of on pixels are counted in each block. (Range: 0 ~ 16) • If # >   1, otherwise 0 • Original 32x32 bitmap is reduced to 8x8 binary matrix. (c) 2000-2005 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr

  4. Introduction to Data (3) • Data • train.txt: 3823 examples • test.txt: 1797 examples • Representation • In the text files, each row consists of 64 binary values with its label attached at 65-th column. • Class distribution (c) 2000-2005 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr

  5. (c) 2000-2005 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr

  6. Preliminary Result • k-nn result (k = 3) on the test set • Accuray: 93.10% (ratio of correctly classified) a b c d e f g h i j <-- classified as 174 0 0 0 1 1 2 0 0 0 | a = 0 0 178 1 0 1 0 2 0 0 0 | b = 1 0 9 167 0 0 0 0 1 0 0 | c = 2 1 2 0 174 0 1 0 1 2 2 | d = 3 0 11 0 0 168 0 0 0 0 2 | e = 4 0 2 0 1 1 172 1 0 0 5 | f = 5 2 1 0 0 0 1 176 0 1 0 | g = 6 0 0 1 0 1 0 0 174 1 2 | h = 7 1 16 4 7 1 6 2 1 132 4 | i = 8 2 2 0 10 0 4 0 1 3 158 | j = 9 (c) 2000-2005 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr

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