Table 1.
The training set data of participants.
Table 2.
The test set information.
Table 3.
The participants with an abnormality in the follow-up.
Table 4.
The features and statistics which were added or removed to the two feature sets.
Fig 1.
Two different hypothetical types of two-dimensional data of the target group and the rest.
The instances shown by the warm-colored circles and the cool-colored triangles are for the target group and the rest, respectively. All instances belonging to a participant have the same color. In (a), all the target group participants’ instances are distinguishable using a classifier. In (b), only some instances of the target group participants are separable from the other instances by a classifier.
Fig 2.
An overall view of WSI and SSI methods.
(a) In WSI method, after feature extraction, a classifier is trained on all instances and majority pooling (MP) is usually used in the testing phase. In this study Best-chance threshold Pooling (BP), which is a threshold-based pooling with the threshold giving the best accuracy on the test set, is also used to give the best chance to WSI classifier. (b) In the proposed SSI classifier, after feature extraction, clustering is applied to find and select exclusive instances containing instances of the target group participants only. Then classifiers are trained using exclusive instances, and a participant is classified in the target group in the testing phase if any classifier detects a positive instance for it.
Table 5.
The number of instances of each participant in the training set that are classified as ASD using each trained SSI classifier.
Fig 3.
Two classifiers trained on the two exclusive clusters found during the SSI classifier training phase.
(a) The Variance of Frame-wise Temporal Derivative (VFTD) of the 7th MFCC coefficient separates 27 instances of 8 ASD subjects from all TD instances of the training set. (b) VFTD of the 6th SONE coefficient separates 17 instances of 7 ASD participants from all TD instances of the training set.
Table 6.
The results of classifiers on the instances of each participant in the test set.
Fig 4.
Instances of several ASD and TD participants scattered in the space of two features given by the proposed SSI method.
The instances of a chosen ASD participant are illustrated in green to show that a participant may have instances in the area common with TD instances besides those two areas separated by the selected thresholds as ASD. The mentioned ASD participant (with green instances) is tagged as ASD, due to having at least one instance with the greater value than at least one of the thresholds on the two features.
Table 7.
Comparison of the results on the test set using the two methods; SSI approach and a baseline approach.
Table 8.
Classification of the participants under 18 months using our trained SSI classifier.