ML-kNN Algorithm Research Articles

The Implementation of Multi Label K- Nearest Neighbor Algorithm To Classifying Essay Answers

One way of assessing essays is to use the STAR (Situation, Task, Action, Result) method. This method aims to classify whether the essay already reflects the values of the situation, task, action, and outcome labels presented by the prospective driving teacher. Multi-label classification refers to a classification task in which each data sample can be classified into multiple categories or labels simultaneously. Various methods have been developed to perform multi-label text classification, such as MLKNN, MLTSVM, and MLP. This research aims to determine the value of the accuracy of the score. Therefore, the Multi-Label K-Nearest Neighbor, or MLKNN, algorithm can be implemented to classify essays with five labels: no star, situation, task, action, and result. With a total dataset of 110, which will be obtained from one of the assessors from the driving teacher. The MLKNN algorithm produces an accuracy rate with an average value of 15.6%.

An Anisotropic Velocity Model for Microseismic Events Localization in Tunnels

The velocity model is one of the main factors affecting the accuracy of microseismic event localization. This paper addresses the issue of the low accuracy of microseismic event localization in tunnels and, combined with active-source technology, proposes a “source–station” velocity model. The velocity model assumes that the velocity from the source to each station is different, and it can greatly improve the accuracy of the time-difference-of-arrival algorithm. At the same time, for the case of multiple active sources, the MLKNN algorithm was selected as the velocity model selection method through comparative testing. The results of numerical simulation and laboratory tests in the tunnel showed that the average location accuracy of the “source–station” velocity model was improved compared with that of the isotropic velocity and sectional velocity models, with numerical simulation experiments improving accuracy by 79.82% and 57.05% (from 13.28 m and 6.24 m to 2.68 m), and laboratory tests in the tunnel improving accuracy by 89.26% and 76.33% (from 6.61 m and 3.00 m to 0.71 m). The results of the experiments showed that the method proposed in this paper can effectively improve the location accuracy of microseismic events in tunnels.

Research on multi-label user classification of social media based on ML-KNN algorithm

Several research studies have been conducted on multi-label classification algorithms for text and images, but few have been conducted on multi-label classification for users. Moreover, the existing multi-label user classification algorithm does not provide an effective representation of users, and it is difficult to use directly in social media scenarios. By analyzing complex social networks, this paper aims to achieve multi-label classification of users based on research in single-label classification.Considering the limitations of existing research, this paper proposes a user topic classification method based on heterogeneous networks as well as a user multi-label classification method based on community detection. The model is trained using the ML-KNN multi-label classification algorithm. In actual scenarios, the algorithm is more effective than existing multi-label classification methods when applied to multi-label classification tasks for social media users.According to the results of the analysis, the algorithm has a high level of accuracy in classifying different theme users into a variety of different scenarios using different theme users. Furthermore, this study contributes to the advancement of classification research by expanding its perspective.

Contributions of MIR to soundscape ecology. Part 3: Tagging and classifying audio features using a multi-labeling k-nearest neighbor approach

Scientists are using acoustic monitoring to assess the impact of altered soundscapes on wildlife communities and human systems. In the soundscape ecology field, monitoring and analyses approaches rely on the interdisciplinary intersection of ecology, acoustics, and computer science. Combining theory and practice of each field in the context of Knowledge Discovery in Databases (KDD), soundscape ecologists provide innovative monitoring solutions for ecologically-driven research questions. We propose a soundscape content analysis framework for improved knowledge outcome with assistance of the new multi-label (ML) concept.Here, we investigated the effectiveness of a ML k-nearest neighbor algorithm (ML-kNN) for labeling concurrent soundscape components within a single recording. We manually labeled 1200 field recordings for the presence of soundscape components and extracted ecological acoustic features, audio profile features, and Gaussian-mixture model features for each recording. Then, we tested the ML-kNN algorithm accuracy with well-established metrics adapted to ML learning.We found that seventeen unique acoustic features could predict a set of biophonic, geophonic, and anthrophonic labels for a single field recording with average precision of 0.767. However, certain labels were predicted incorrectly depending on the time of day and co-occurrence of that label with another label, suggesting further refinement is needed to improve the accuracy of predicted labels.Overall, this ML classification approach could enable researchers to label field recordings more quickly and generate an “alert” system for monitoring changes in a specific sound class. Ultimately, the adaptation of the ML algorithm may provide soundscape ecologists with new metadata labels that are searchable in large databases of soundscape field recordings.

A classification algorithm based on weighted ML-kNN for multi-label data

The ML-kNN algorithm uses naive Bayesian classification to modify the traditional kNN algorithm to solve multi-label classification problems. However, the ML-kNN algorithm is prone to misjudgement or incomplete judgment of the unseen instance's label set in two special cases: when the number of labels in the training set is not balanced and when the training instances are unevenly distributed in space. Therefore, a weighted ML-kNN algorithm (i.e., wML-kNN) is proposed in this paper. The main idea is to assign different weights to each label according to the proportion of labels and mutual information of the spatial distribution of unseen instances to training instances. This method can reduce the probability of misjudgement of the unseen instance's label set. A comparative study was conducted on four multi-label datasets that included review classification and three other published benchmark multi-label datasets: yeast gene function analysis, natural scene classification, and musical sentiment classification. The results show that the performance of the wML-kNN algorithm is better than the other four multi-label learning algorithms, including ML-kNN.

An Improved ML-kNN Algorithm by Fusing Nearest Neighbor Classification

Based on the ML-kNN multi-label classification algorithm of probability and statistics, the k neighbors of unclassified sample are implicitly deemed that they have the same effect on classification result while ignoring the influence of the distances between k neighbors and unclassified sample. This paper proposes an improved ML-kNN algorithm by fusing nearest neighbor classification: IML-kNN. On the basis of the traditional ML-kNN algorithm, the algorithm considers the influence of the nearest neighbor and k neighbors of unclassified sample. Numerical simulation results show that the IML-kNN algorithm can have a good classification effect on the multi-label evaluation metrics.

Ml-KNN Algorithm Based on Frequent Item Sets

In order to solve the problem of ignoring the correlation between class labels, this paper describes a new method for multi-label classification based on the frequent item sets to classify an unseen instance on the basis of its k nearest neighbors ( MLFI-KNN). For each unseen instance, MLFI-KNN takes its k-nearest neighbors in the training set and counts the number of occurrences of each label in this neighborhood, and then utilizes the FP-growth algorithm to obtain the frequent item sets between the labels that these neighboring instances include, in order to determine the predicted label set. Experiments on benchmark dataset demonstrate the effectiveness of the proposed approach as compared to some existing well-known methods.

Feature selection for gene function prediction using multi-labelled lazy learning

In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, feature selection for the multi-label method was proposed based on mutual information. In detail, we use the distribution of mutual information for feature selection in the multi-label problems. Our experiment was preceded on a multi-label lazy learning approach named ML-kNN, which is derived from the traditional k-Nearest Neighbour (KNN) algorithm. Experimental results on a real-world multi-label bioinformatics data show that ML-kNN with feature selection greatly outperforms the prior ML-kNN algorithm.