ＮＢ朴素贝叶斯算法在UCI数据集上的的java实现

1. Title: Chess Endgame Database for White King and Rook against Black King (KRK) - Black-to-move Positions Drawn or Lost in N Moves. 2. Source Information: -- Creators: Database generated by Michael Bain and Arthur van Hoff at the Turing Institute, Glasgow, UK. -- Donor: Michael Bain (mike@cse.unsw.edu.au), AI Lab, Computer Science, University of New South Wales, Sydney 2052, Australia. (tel) +61 2 385 3939 (fax) +61 2 663 4576 -- Date: June, 1994. 3. Past Usage: Chess endgames are complex domains which are enumerable. Endgame databases are tables of stored game-theoretic values for the enumerated elements (legal positions) of the domain. The game-theoretic values stored denote whether or not positions are won for either side, or include also the depth of win (number of moves) assuming minimax-optimal play. From the point of view of experiments on computer induction such databases provide not only a source of examples but also an oracle (Roycroft, 1986) for testing induced rules. However a chess endgame database differs from, say, a relational database containing details of parts and suppliers in the following important respect. The combinatorics of computing the required game-theoretic values for individual position entries independently would be prohibitive. Therefore all the database entries are generated in a single iterative process using the ``standard backup'' algorithm (Thompson, 1986). A KRK database was described by Clarke (1977). The current database was described and used for machine learning experiments in Bain (1992; 1994). It should be noted that our database is not guaranteed correct, but the class distribution is the same as Clarke's database. In (Bain 1992; 1994) the task was classification of positions in the database as won for white in a fixed number of moves, assuming optimal play by both sides. The problem was structured into separate sub-problems by depth-of-win ordered draw, zero, one, ..., sixteen. When learning depth d all examples at depths > d are used as negatives. Quinlan (1994) applied Foil to learn a complete and correct solution for this task. The typical complexity of induced classifiers in this domain suggest that the task is demanding when background knowledge is restricted. 4. Relevant Information: An Inductive Logic Programming (ILP) or relational learning framework is assumed (Muggleton, 1992). The learning system is provided with examples of chess positions described only by the coordinates of the pieces on the board. Background knowledge in the form of row and column differences is also supplied. The relations necessary to form a correct and concise classifier for the target concept must be discovered by the learning system (the examples already provide a complete extensional definition). The task is closely related to Quinlan's (1983) application of ID3 to classify White King and Rook against Black King and Knight (KRKN) positions as lost 2-ply or lost 3-ply. The framework is similar in that the example positions supply only low-grade data. An important difference is that additional background predicates of the kind supplied in the KRKN study via hand-crafted attributes are not provided for this KRK domain. 5. Number of Instances: 28056 6. Number of Attributes: There are six attribute variables and one class variable. 7. Attribute Information: 1. White King file (column) 2. White King rank (row) 3. White Rook file 4. White Rook rank 5. Black King file 6. Black King rank 7. optimal depth-of-win for White in 0 to 16 moves, otherwise drawn {draw, zero, one, two, ..., sixteen}. 8. Missing Attribute Values: None 9. Class Distribution: draw 2796 zero 27 one 78 two 246 three 81 four 198 five 471 six 592 seven 683 eight 1433 nine 1712 ten 1985 eleven 2854 twelve 3597 thirteen 4194 fourteen 4553 fifteen 2166 sixteen 390 Total 28056 10. Note: Foil is available by anonymous ftp from ftp.cs.su.oz.au, file pub/foil6.sh. References: (BibTeX format) @incollection{bain_1992, AUTHOR = "M. Bain", TITLE = "Learning optimal chess strategies", BOOKTITLE = "{ILP 92}: {P}roc. {I}ntl. {W}orkshop on {I}nductive {L}ogic {P}rogramming", YEAR = 1992, VOLUME = "ICOT TM-1182", EDITOR = "S. Muggleton", PUBLISHER = "Institute for New Generation Computer Technology", ADDRESS = "Tokyo, Japan"} @phdthesis{bain_1994, TITLE = "Learning {L}ogical {E}xceptions in {C}hess", AUTHOR = "M. Bain", SCHOOL = "University of Strathclyde", YEAR = "1994"} @incollection{clarke_1977, AUTHOR = "M. R. B. Clarke", TITLE = "A {Q}uantitative {S}tudy of {K}ing and {P}awn {A}gainst {K}ing", BOOKTITLE = "Advances in Computer Chess", VOLUME = 1, PAGES = "108--118", EDITOR = "M. R. B. Clarke", PUBLISHER = "Edinburgh University Press", ADDRESS = "Edinburgh", YEAR = "1977"} @incollection{muggleton_1992, AUTHOR = "S. Muggleton", TITLE = "Inductive {L}ogic {P}rogramming", BOOKTITLE = "Inductive {L}ogic {P}rogramming", PAGES = "3--27", EDITOR = "S. Muggleton", PUBLISHER = "Academic Press", ADDRESS = "London", YEAR = "1992"} @incollection{quinlan_1983, AUTHOR = "J. R. Quinlan", TITLE = "Learning {E}fficient {C}lassification {P}rocedures and their {A}pplication to {C}hess {E}nd {G}ames", YEAR = 1983, PAGES = "464--482", BOOKTITLE = "Machine Learning: An Artificial Intelligence Approach", EDITOR = "R. Michalski and J. Carbonnel and T. Mitchell", PUBLISHER = "Tioga", ADDRESS = "Palo Alto, CA"} @misc{quinlan_1994, AUTHOR = "J. R. Quinlan", YEAR = 1994, NOTE = "Personal Communication"} @article{roycroft_1986, AUTHOR = "A. J. Roycroft", TITLE = "Database ``{O}racles'': {N}ecessary and desirable features", JOURNAL = "International Computer Chess Association Journal", YEAR = "1986", VOLUME = 8, NUMBER = 2, PAGES = "100--104"} @article{thompson_1986, AUTHOR = "K. Thompson", TITLE = "Retrograde {A}nalysis of {C}ertain {E}ndgames", JOURNAL = "International Computer Chess Association Journal", YEAR = "1986", VOLUME = "8", NUMBER = "3", PAGES = "131--139"}