TY - GEN
T1 - Applying fuzzy hypothesis testing to medical data
AU - Last, Mark
AU - Schenker, Adam
AU - Kandel, Abraham
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1999.
PY - 1999/1/1
Y1 - 1999/1/1
N2 - Classical statistics and many data mining methods rely on “statistical significance” as a sole criterion for evaluating alternative hypotheses. In this paper, we use a novel, fuzzy logic approach to perform hypothesis testing. The method involves four major steps: Hypothesis formulation, data selection (sampling), hypothesis testing (data mining), and decision (results). In the hypothesis formulation step, a null hypothesis and set of alternative hypotheses are created using conjunctive antecedents and consequent functions. In the data selection step, a subset D of the set of all data in the database is chosen as a sample set. This sample should contain enough objects to be representative of the data to a certain degree of satisfaction. In the third step, the fuzzy implication is performed for the data in D for each hypothesis and the results are combined using some aggregation function. These results are used in the final step to determine if the null hypothesis should be accepted or rejected. The method is applied to a real-world data set of medical diagnoses. The automated perception approach is used for comparing the mapping functions of fuzzy hypotheses, tested on different age groups (“young” and “old”). The results are compared to the “crisp” hypothesis testing.
AB - Classical statistics and many data mining methods rely on “statistical significance” as a sole criterion for evaluating alternative hypotheses. In this paper, we use a novel, fuzzy logic approach to perform hypothesis testing. The method involves four major steps: Hypothesis formulation, data selection (sampling), hypothesis testing (data mining), and decision (results). In the hypothesis formulation step, a null hypothesis and set of alternative hypotheses are created using conjunctive antecedents and consequent functions. In the data selection step, a subset D of the set of all data in the database is chosen as a sample set. This sample should contain enough objects to be representative of the data to a certain degree of satisfaction. In the third step, the fuzzy implication is performed for the data in D for each hypothesis and the results are combined using some aggregation function. These results are used in the final step to determine if the null hypothesis should be accepted or rejected. The method is applied to a real-world data set of medical diagnoses. The automated perception approach is used for comparing the mapping functions of fuzzy hypotheses, tested on different age groups (“young” and “old”). The results are compared to the “crisp” hypothesis testing.
KW - Approximate reasoning
KW - Data mining
KW - Fuzzy set theory
KW - Hypothesis testing
KW - Knowledge discovery in databases
UR - https://www.scopus.com/pages/publications/84958036730
U2 - 10.1007/978-3-540-48061-7_27
DO - 10.1007/978-3-540-48061-7_27
M3 - Conference contribution
AN - SCOPUS:84958036730
SN - 3540666451
SN - 9783540666455
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 221
EP - 229
BT - New Directions in Rough Sets, Data Mining, and Granular-Soft Computing - 7th International Workshop, RSFDGrC 1999, Proceedings
A2 - Ohsuga, Setsuo
A2 - Zhong, Ning
A2 - Skowron, Andrzej
PB - Springer Verlag
T2 - 7th International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, RSFDGrC 1999
Y2 - 9 November 1999 through 11 November 1999
ER -