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A Rule-Based Approach to Multiple Statistical Test Analysis

END-OF FILE RECEIVED, NO MORĘ DATA.

1200 SETS OF DATA WERE TESTED.

1.193000000E+003 DETECTIONS WERE RECEIVED.

FINAŁ RUN RESULTS:

SYSTEM:    INDIYIDUAL TESTS:

U CORRECT DETERMINATIONS	=1129	U CORRECT DETECTIONS	=1067
it FIND VERIFICATIONS	=1037
U MISFIRE VERIFICATIONS	=92
# INCORRECT DETERMINATIONS	=71	U TEST MISFIRES	=127
# MISSES	=29
# MISFIRES	=35

SUCCESS RATĘ =    9.464000e-001 TEST SUCCESS RATĘ =    8.944000e-001

Figurę 9. Sample System Finał Run Summary.

Oyerall Results

For the two detection scenarios—sudden shift and conti nuous drift—the speed of detection comparison favored the rule system. For these two cases, the system provided for a correct decision ąuicker than the baseline. Also, for both scenarios, the rule system slightly outperformed the baseline test on accuracy. The system averaged 95.02% success in correctly classifying incoming signals. This is compared to the 94.57% success achieved by the CUSUM baseline test. Both methods obtained 93.0% success for the nuli case, and almost were identical in the continuous drift case as well. The system also matched the baseline—96.64% to 95.62%—for the sudden shift case. Figurę 10 summarizes the system's success, along with the baseline's, for the three test cases. Also, performance data for the rules and rulesets are given. (Notę: The totals for the individual rules may not equal the ruleset totals, sińce multiple rules may backup a given incoming signal.)

Indiyidual Scenario Findings

In both the sudden shift and continuous drift cases, the same time-between-events data was used for both the system and baseline analysis. The emphasis in each case was the system's speed of detection and accuracy of detection performance.

Sudden Shift Case

In both of the performance measurements, the system outperformed the baseline method. As shown in Figurę 10, the system bested the baseline