To overcome the limitations of the FSIP (feature selection based on information gain and Pearson correlation coefficient) feature selection algorithm that need human to determine the borderline to detect the feature subsets, the totally adaptive 2D feature selection algorithm is proposed in this study based on discernibility matrix. It is referred to as DFSIP (discernibility based FSIP). DFSIP introduces discernibility matrix into the feature selection process of FSIP. It first initializes the candidate feature set comprising all features and constructs the initial discernibility matrix, then it detects the most significant feature from the current candidate feature set, so as to add it to feature subset and use it to reduce the discernibility matrix. After that the candidate feature set is updated using the union of the cells of the reduced discernibility matrix, and the most significant feature is detected from the current candidate feature set again, so as to put it into the feature subset and use it to reduce the discernibility matrix, and the candidate feature set is updated again. This process repeats till there is not any feature left in the candidate feature set. The power of DFSIP is tested on very famous gene expression datasets, and its performance is compared with that of the popular feature selection algorithms including FSIP, mRMR, LLE Score, DRJMIM, AVC, and AMID by comparing the performance of the K-ELM classifier built using the feature subset detected by these feature selection algorithms. In addition, the significant test is done to verify whether or not there is the significant difference between DFSIP and FSIP as well as other compared feature selection algorithms. The experimental results demonstrate that DFSIP is superior to the compared ones, especially it has the significant difference to LLE Score, DRJMIM, and AMID feature selection algorithms. Although there is not significant difference between DFSIP and FSIP, it defeats FSIP in performance. It can be concluded that DFSIP can totally adaptively detect the feature subset with sound classification capability.