Machine learning based analysis of gender differences in visual inspection decision making

Bousquet, 2002, Stability and generalization, The Journal of Machine Learning Research, 2, 499 Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324 Breiman, 1993 Busch, 1995, Gender differences in self-efficacy and attitudes toward computers, Educational Computing Research, 12, 147, 10.2190/H7E1-XMM7-GU9B-3HWR Chin, 1982, Automated visual inspection: a survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, 4, 557, 10.1109/TPAMI.1982.4767309 Cohen, 1988 Czerwinski, 2002, Women take a wider view, 195 Drury, 1978, Integrating human factors models into statistical quality control, Human Factors: The Journal of the Human Factors and Ergonomics Society, 20, 561, 10.1177/001872087802000506 Drury, 1981, Training programs for inspection, Human Factors: The Journal of the Human Factors and Ergonomics Society, 23, 473, 10.1177/001872088102300410 C.G. Drury, J. Watson, Good Practices in Visual Inspection, Technical Report, Federal Aviation Administration/Office of Aviation Medicine, Washington DC, 2002. Duda, 2000 Eitzinger, 2009, Assessment of the influence of adaptive components in trainable surface inspection systems, Machine Vision and Applications, 21, 613, 10.1007/s00138-009-0211-1 Fern, 2010, Mining problem-solving strategies from HCI data, ACM Transactions on Computer–Human Interaction, 17, 10.1145/1721831.1721834 Fisher, 1966 Gallwey, 1986, Task complexity in visual inspection, Human Factors: The Journal of the Human Factors and Ergonomics Society, 28, 595, 10.1177/001872088602800509 Garrett, 2001, The effects of per-lot and per-item pacing on inspection performance, International Journal of Industrial Ergonomics, 27, 291, 10.1016/S0169-8141(00)00057-3 Gramopadhye, 1997, Noise, feedback training, and visual inspection performance, International Journal of Industrial Ergonomics, 20, 223, 10.1016/S0169-8141(96)00051-0 Halpern, 2000 Harris, 1969 Hastie, 2009 W. Heidl, S. Thumfart, E. Lughofer, C. Eitzinger, E.P. Klement, Classifier-based analysis of visual inspection: gender differences in decision-making, in: Proceedings of SMC2010, IEEE Conference on Systems, Man and Cybernetics, pp. 113–120. Hyde, 2005, The gender similarities hypothesis, The American psychologist, 60, 581, 10.1037/0003-066X.60.6.581 Lehto, 2007 Ludbrook, 1998, Why permutation tests are superior to t and f tests in biomedical research, The American Statistician, 52, 127 Lughofer, 2009, On human-machine interaction during on-line image classifier training, IEEE Transactions on Systems, Man and Cybernetics, Part A – Systems and Humans, 39, 960, 10.1109/TSMCA.2009.2025025 Malamas, 2003, A survey on industrial vision systems, applications and tools, Image and Vision Computing, 21, 171, 10.1016/S0262-8856(02)00152-X Moffat, 1998, Navigation in a “virtual” maze: sex differences and correlation with psychometric measures of spatial ability in humans, Evolution and Human Behavior, 19, 73, 10.1016/S1090-5138(97)00104-9 Newman, 1995, A survey of automated visual inspection, Computer Vision and Image Understanding, 61, 231, 10.1006/cviu.1995.1017 Neyman, 1933, On the problem of the most efficient tests of statistical hypotheses, Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 231, 289, 10.1098/rsta.1933.0009 Schneeweiss, 1989 Schölkopf, 2001 Skrondal, 2004 Stevens, 2005, Machine learning models of problem space navigation: the influence of gender, Computer Science and Information Systems/ComSIS, 2, 83, 10.2298/CSIS0502083S Tan, 2003, Women go with the (optical) flow, 209 Vapnik, 1999