Based on the given feature "TIME_B_S" and the task of determining whether the myocardial infarction complications data shows chronic heart failure, we can analyze the relationship between the two.

Since the feature "TIME_B_S" represents the time elapsed from the beginning of the attack of coronary heart disease (CHD) to the hospital, it is possible that the time duration could have an impact on the occurrence of chronic heart failure as a myocardial infarction complication.

To analyze this relationship, we can examine the distribution of the feature values for each target class (yes or no for chronic heart failure) in the data. From this analysis, we can identify the possible values of TIME_B_S for each target class.

Based on the analysis, the resulting dictionary would look like:

```json
{
	"no": ["less than 2 hours", "2-4 hours", "4-6 hours", "6-8 hours", "8-12 hours"], 
	"yes": ["more than 3 days", "more than 2 days", "12-24 hours", "more than 1 days"]
}
```

Note that in the "no" class, we include values that indicate shorter time durations since they are less likely to be associated with the occurrence of chronic heart failure. Similarly, in the "yes" class, we include values that indicate longer time durations since they might be more likely to be associated with the occurrence of chronic heart failure.

The values "more than 3 days" and "more than 2 days" indicate longer time durations, which could potentially contribute to the occurrence of chronic heart failure as a myocardial infarction complication. The values "12-24 hours" and "more than 1 days" also indicate relatively longer time durations, which might have a partial influence on chronic heart failure occurrence.