Fault prediction aims to identify whether a software module is defect-prone or not according to metrics that are mined from software projects. These metric values, also known as features, may involve irrelevance and redundancy, which hurt the performance of fault prediction models. In order to filter out irrelevant and redundant features, a Hybrid Feature Selection (abbreviated as HFS) method for software fault prediction is proposed. The proposed HFS method consists of two major stages. First, HFS groups features with hierarchical agglomerative clustering; second, HFS selects the most valuable features from each cluster to remove irrelevant and redundant ones based on two wrapper based strategies. The empirical evaluation was conducted on 11 widely-studied NASA projects, using three different classifiers with four performance metrics (precision, recall, F-measure, and AUC). Comparison with six filter-based feature selection methods demonstrates that HFS achieves higher average F-measure and AUC values. Compared with two classic wrapper feature selection methods, HFS can obtain a competitive prediction performance in terms of average AUC while significantly reducing the computation cost of the wrapper process.

The task of Speech Emotion Detection (SED) aims at judging positive class and negetive class when the speaker expresses emotions. The SED performances are heavily dependent on the diversity and prominence of emotional features extracted from the speech. However, most of the existing related research focuses on investigating the effects of single feature source and hand-crafted features. Thus, we propose a SED approach using multi-source low-level information based recurrent branches. The fusion multi-source low-level information obtain variety and discriminative representations from speech emotion signals. In addition, focal-loss function benifit for imbalance classes, resulting in reducing the proportion of well-classified samples and increasing the weights for difficult samples on SED tasks. Experiments on IEMOCAP corpus demonstrate the effectiveness of the proposed method. Compared with the baselines, MSIR achieve the significant performance improvements in terms of Unweighted Average Recall and F1-score.