RIFS: a randomly restarted incremental feature selection algorithm

Abstract

The advent of big data era has imposed both running time and learning efficiency challenges for the machine learning researchers. Biomedical OMIC research is one of these big data areas and has changed the biomedical research drastically. But the high cost of data production and difficulty in participant recruitment introduce the paradigm of “large p small n” into the biomedical research. Feature selection is usually employed to reduce the high number of biomedical features, so that a stable data-independent classification or regression model may be achieved. This study randomly changes the first element of the widely-used incremental feature selection (IFS) strategy and selects the best feature subset that may be ranked low by the statistical association evaluation algorithms, e.g. t-test. The hypothesis is that two low-ranked features may be orchestrated to achieve a good classification performance. The proposed Randomly re-started Incremental Feature Selection (RIFS) algorithm demonstrates both higher classification accuracy and smaller feature number than the existing algorithms. RIFS also outperforms the existing methylomic diagnosis model for the prostate malignancy with a larger accuracy and a lower number of transcriptomic features.

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis

“…Three classification performance measurements, i.e., accuracy (Acc), sensitivity (Sn), and specificity (Sp), were used to evaluate how well a feature subset performed (Ye et al, 2017;Xu et al, 2018;Yokoi et al, 2018;Zhao et al, 2018). The RA children were regarded as the positive samples (P) while the matched controls were the negative samples (N).…”

“…These measurements were used in various prediction models like the DNA and RNA functional elements (He et al, 2018;Feng et al, 2019). And they were calculated using the 10-fold cross-validation (10FCV) strategy as similar in Ye et al (2017) and Zhao et al (2018).…”

ELMO: An Efficient Logistic Regression-Based Multi-Omic Integrated Analysis Method for Breast Cancer Intrinsic Subtypes

“…Recursive feature elimination (RFE) was a widelyused feature selection framework [45] that may use different classifiers as the feature evaluator, e.g., support vector machine (SVM-RFE) [46], decision tree (DT-RFE) and logistic regression (LR-RFE), etc. Other investigated wrappers were extremely randomized tree (ET) [47], gradient boosted decision tree (GBDT), xgboost (XGB), random forest (RF) [48], McTwo [27] and RIFS [26].…”

sefOri: selecting the best-engineered sequence features to predict DNA replication origins

“…Two more popular feature selection algorithms were evaluated. T-test based feature ranking algorithm was widely used to evaluate the phenotype-association of each feature, and usually the incremental feature selection (IFS) was utilized to find the best number of top-ranked features (Gharbali, et al, 2018;Ye, et al, 2017). The Lasso algorithm evaluated the features by minimizing the L1-penalty and assigned a weight to each feature (Deshpande, et al, 2019;Kumar, et al, 2017).…”