The phrase "a good subset of" is correct and usable in written English.
It can be used when referring to a portion of a larger group that possesses desirable qualities or characteristics.
Example: "The research team found that a good subset of the participants showed significant improvement after the intervention."
Alternatives: "a valuable portion of" or "a beneficial segment of".
Therefore, we propose here a polynomial time heuristic algorithm, namely, Transmission Mode Generating Algorithm 2, or shortly, TMGA2, that can generate a good subset of 's for each.
In the main stream of this theory, the problem is to select a good subset of allocations under perfect knowledge of the characteristics of the population and of the feasible set.
Neighbourhood control can be used to filter a good subset of neighbours based on network conditions (e.g. Link Quality Indicator or Received Signal Strength Indication--RSSI), neighbour information (e.g. remaining energy, network ID, etc)., topology constraints and/or application requirements.
The challenge for companies like YPlan, in addition to being mobile-only – (something that forgets how many office workers today still plan their weekend from their desk) – is making sure that it's not overlooking events in its curation that would appeal to a good subset of its users.
To construct a good subset of SES predictors, household-level information on assets and education level were combined.
We are now interested in finding a good subset of F q, r that does not contain any pairwise conflicts in the above sense.
Stepwise logistic regression model was used to find the good subset of statistically significant predictors of infection used in the Table 5 (Table 6).
For each fold k = 1,2,..,K a) Find a subset of "good" predictors that show fairly strong (univariate) correlation with the class labels, using all of the samples except those in fold k. b) Using just this subset of predictors, build a multivariate classifier, using all of the samples except those in fold k. c) Use the classifier to predict the class labels for the sample in fold k. .
We argue that, in order to scale to very large dimensions and effectively exploit the potentials of a MapReduce-like framework, an effective associative classifier should aim at reducing, if not eliminating, the contribution of these techniques to the reduction of the model size, and focus on the extraction of a small, good quality subset of the rules.
Kohavi and John (hereafter KJ) [ 41] addressed the issue of finding a good feature subset and its relation to which features shall be included by partitioning features into three types of strong relevance, weak relevance, and irrelevance.
Mei et al. [79] has shown that selecting a good feature subset can significantly improve the performance of GP, i.e. finding better scheduling heuristics.
