TY - GEN
T1 - Refining rules incorporated into knowledge-based support vector learners via successive linear programming
AU - Maclin, Richard
AU - Wild, Edward
AU - Shavlik, Jude
AU - Torrey, Lisa
AU - Walker, Trevor
PY - 2007
Y1 - 2007
N2 - Knowledge-based classification and regression methods are especially powerful forms of learning. They allow a system to take advantage of prior domain knowledge supplied either by a human user or another algorithm, combining that knowledge with data to produce accurate models. A limitation of the use of prior knowledge occurs when the provided knowledge is incorrect. Such knowledge likely still contains useful information, but knowledge-based learners might not be able to fully exploit such information. In fact, incorrect knowledge can lead to poorer models than result from knowledge-free learners. We present a support-vector method for incorporating and refining domain knowledge that not only allows the learner to make use of that knowledge, but also suggests changes to the provided knowledge. Our approach is built on the knowledge-based classification and regression methods presented by Fung, Mangasarian, & Shavlik (2002; 2003) and by Mangasarian, Shavlik, & Wild (2004). Experiments on artificial data sets with known properties, as well as on a real-world data set, demonstrate that our method learns more accurate models while also adjusting the provided rules in intuitive ways. Our new algorithm provides an appealing extension to knowledge-based, support-vector learning that is not only able to combine knowledge from rules with data, but is also able to use the data to modify and change those rules to better fit the data.
AB - Knowledge-based classification and regression methods are especially powerful forms of learning. They allow a system to take advantage of prior domain knowledge supplied either by a human user or another algorithm, combining that knowledge with data to produce accurate models. A limitation of the use of prior knowledge occurs when the provided knowledge is incorrect. Such knowledge likely still contains useful information, but knowledge-based learners might not be able to fully exploit such information. In fact, incorrect knowledge can lead to poorer models than result from knowledge-free learners. We present a support-vector method for incorporating and refining domain knowledge that not only allows the learner to make use of that knowledge, but also suggests changes to the provided knowledge. Our approach is built on the knowledge-based classification and regression methods presented by Fung, Mangasarian, & Shavlik (2002; 2003) and by Mangasarian, Shavlik, & Wild (2004). Experiments on artificial data sets with known properties, as well as on a real-world data set, demonstrate that our method learns more accurate models while also adjusting the provided rules in intuitive ways. Our new algorithm provides an appealing extension to knowledge-based, support-vector learning that is not only able to combine knowledge from rules with data, but is also able to use the data to modify and change those rules to better fit the data.
UR - http://www.scopus.com/inward/record.url?scp=36348967725&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=36348967725&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:36348967725
SN - 1577353234
SN - 9781577353232
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 584
EP - 589
BT - AAAI-07/IAAI-07 Proceedings
T2 - AAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference
Y2 - 22 July 2007 through 26 July 2007
ER -