Page 32 - Kỷ yếu hội thảo khoa học lần thứ 12 - Công nghệ thông tin và Ứng dụng trong các lĩnh vực (CITA 2023)

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the best offspring produced by different training objectives (i.e., mutations). In this
way, it contributes to progress in and the success of GANs. Experiments on several
datasets demonstrate the advantages of integrating different adversarial training objec-
tives and E- ormance for image generation.Dealing with missing
data is a common issue in empirical research. Data scientists encounter various types
of missing data, such as Missing Completely At Random (MCAR), Missing At Random
(MAR), and Missing Not At Random (MNAR), which are classified based on the mech-
anisms of missing data. MCAR occurs when the missingness is unrelated to the hypo-
thetical value, values of other variables, or observed records. In MAR, on the other
hand, missing data points are unrelated to the specific missing values, but may depend
on a subset of observed data. Lastly, MNAR is the ultimate type of missing data, and it
occurs when missing data points depend on both hypothetical values and specific vari-
able values.

3 The proposed method

As state before, an evolutionary step in EGAN can exploit the advantages and suppress
the weaknesses of different metrics. Meanwhile, using GAN for imputing missing value
archived high quality in comparison with state-of-the-art imputation methods. There-
fore, in this paper, we proposed an imputation method by integrating GAN and evolu-
tionary computation, which is called EGAIN ( Evolutionary Generative Adversarial for
Imputation Data). Similar to EGAN, in our EGAIN model, a population of genera-
tors is evolves in a given environment discriminator . Each evolution-
ary step consists of three sub-stages:
Variation: Applying one step of an optimal algorithm with different ob-
jective functions on each to produce generators .
Evaluation: For each generators (i.e., each child), its performance is evalu-
ated by a fitness function consist of two components: quality score and di-
versity score.
Selection: generators with the highest fitness score is kept and evolve to
the next iteration.
The discriminator is updated after each evolutionary step to continually provide the
adaptive losses to drive the population of generator(s) evolving to produce better solu-
tions. Next, our network and the evolutionary step are represented in detail.

3.1 Generator

Suppose that is a missing data in -dimensional space,
is a mask vector indicating which components of are ob-
served, that is:

CITA 2023 ISBN: 978-604-80-8083-9