Semi-supervised feature selection of educational data mining for student performance analysis

In recent years, the informatization of the educational system has caused a substantial increase in educational data. Educational data mining can assist in identifying the factors influencing students’ performance. However, two challenges have arisen in the field of educational data mining: (1) How to handle the abundance of unlabeled data? (2) How to identify the most crucial characteristics that impact student performance? In this paper, a semi-supervised feature selection framework is proposed to analyze the factors influencing student performance. The proposed method is semi-supervised, enabling the processing of a considerable amount of unlabeled data with only a few labeled instances. Additionally, by solving a feature selection matrix, the weights of each feature can be determined, to rank their importance. Furthermore, various commonly used classifiers are employed to assess the performance of the proposed feature selection method. Extensive experiments demonstrate the superiority of the proposed semi-supervised feature selection approach. The experiments indicate that behavioral characteristics are significant for student performance, and the proposed method outperforms the state-of-the-art feature selection methods by approximately 3.9% when extracting the most important feature.</p

Projected cross-view learning for unbalanced incomplete multi-view clustering

Incomplete multi-view clustering (IMVC) aims to partition samples into different groups for datasets with missing samples. The primary goal of IMVC is to effectively address the challenge posed by missing information in clustering analysis. Most existing IMVC methods focus on balanced incomplete multi-view data, assuming a uniform missing rate across all views. However, this assumption does not accurately reflect real-life scenarios. In reality, unbalanced incomplete multi-view data, characterized by varying missing rates among different views, is more prevalent. This presents significant challenges to the clustering process, as varying missing rates can lead to information imbalance. To address these challenges, this paper introduces a novel approach called projected cross-view learning for unbalanced incomplete multi-view clustering (PCL_UIMVC). Specifically, a reconstruction term is integrated, which leverages the information from the existing samples to facilitate the completion of the unbalanced incomplete multi-view data. Next, a projection matrix is incorporated into the model to harmonize feature dimensions across views, mitigating the impact of information imbalance. Then, a graph regularization term is integrated to preserve the geometric structure of the original data. Finally, an iterative algorithm is developed to solve the proposed model. Extensive experiments on eight standard datasets, featuring various rates of missing data, validate the superior clustering performance of the proposed method.</p

Comparison of FISH and IHC in 430 patients and comparison of FISH and RT-PCR on detecting <i>ALK</i> rearrangements among 200 patients.

<p>Comparison of FISH and IHC in 430 patients and comparison of FISH and RT-PCR on detecting <i>ALK</i> rearrangements among 200 patients.</p

Univariate and multivariate analysis for overall survival.

<p>Univariate and multivariate analysis for overall survival.</p

Progression-free survival (PFS) and overall survival (OS) among <i>ALK-</i>positive patients, patients who have <i>EGFR</i> activating mutations and wild type both <i>ALK</i> and <i>EGFR</i> (WT/WT).

<p>(A) PFS for patients receiving the first-line chemotherapy harboring <i>ALK</i> rearrangements, <i>EGFR</i> activating mutations, and WT/WT. (B) PFS for patients receiving EGFR TKIs harboring <i>ALK</i> rearrangements, <i>EGFR</i> activating mutations, and WT/WT. (C) OS for patients harboring <i>ALK</i> rearrangements, <i>EGFR</i> activating mutations, and WT/WT.</p

Detection of <i>ALK</i> rearrangements using Ventana immunohistochemistry (IHC) (200×).

<p>(A) An <i>ALK-</i>negative case without cytoplasmic staining. (B) An <i>EML4-ALK-</i>positive case with strong granular cytoplasmic staining. (C) A <i>KIF5B-ALK-</i>positive case identified by RT-PCR with strong granular cytoplasmic staining.</p

Detection of <i>ALK</i> rearrangements using Fluorescent in situ hybridization (FISH) (1000×).

<p>(A) An <i>ALK-</i>negative case. (B) An <i>ALK-</i>positive case with split signal pattern. (C) An <i>ALK-</i>positive case with isolated red signal pattern.</p

Characteristics of all patients.

<p>Characteristics of all patients.</p