  Naive Bayes(NB) is one of the essential algorithms in data mining. However,
it is rarely used in reality because of the attribute independent assumption.
Researchers have proposed many improved NB methods to alleviate this
assumption. Among these methods, due to high efficiency and easy
implementation, the filter attribute weighted NB methods receive great
attentions. However, there still exists several challenges, such as the poor
representation ability for single index and the fusion problem of two indexes.
To overcome above challenges, we propose a general framework for Adaptive
Two-index Fusion attribute weighted NB(ATFNB). Two types of data description
category are used to represent the correlation between classes and attributes,
intercorrelation between attributes and attributes, respectively. ATFNB can
select any one index from each category. Then, we introduce a switching factor
\{beta} to fuse two indexes, which can adaptively adjust the optimal ratio of
the two index on various datasets. And a quick algorithm is proposed to infer
the optimal interval of switching factor \{beta}. Finally, the weight of each
attribute is calculated using the optimal value \{beta} and is integrated into
NB classifier to improve the accuracy. The experimental results on 50 benchmark
datasets and a Flavia dataset show that ATFNB outperforms the basic NB and
state-of-the-art filter weighted NB models. In addition, the ATFNB framework
can improve the existing two-index NB model by introducing the adaptive
switching factor \{beta}. Auxiliary experimental results demonstrate the
improved model significantly increases the accuracy compared to the original
model without the adaptive switching factor \{beta}.
