Large scale instance selection by means of federal instance selection

Haro-García, Aida de; García-Pedrajas, Nicolás; Castillo, Juan A. Romero del

doi:10.1016/j.datak.2012.03.002

Cited by 17 publications

(2 citation statements)

References 28 publications

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“…For this reason, a binary tree algorithm based on principal component analysis is proposed [15], which divides the current data based on the score of the first m principal components and the corresponding median value. In addition, there also exist some data partition algorithms based on the structure of the nearest neighbor graph and hash approximation [16][17][18][19]. However, most of the existing data partitioning algorithms do not theoretically study the effect of data partitioning on the kNN classification algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Large-Scale $k$ -Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation

Song

Kong

Zhang

2022

Wireless Communications and Mobile Computing

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Owing to the absence of hypotheses of the underlying distributions of the data and the strong generation ability, the k -nearest neighbor (kNN) classification algorithm is widely used to face recognition, text classification, emotional analysis, and other fields. However, kNN needs to compute the similarity between the unlabeled instance and all the training instances during the prediction process; it is difficult to deal with large-scale data. To overcome this difficulty, an increasing number of acceleration algorithms based on data partition are proposed. However, they lack theoretical analysis about the effect of data partition on classification performance. This paper has made a theoretical analysis of the effect using empirical risk minimization and proposed a large-scale k -nearest neighbor classification algorithm based on neighbor relationship preservation. The process of searching the nearest neighbors is converted to a constrained optimization problem. Then, it gives the estimation of the difference on the objective function value under the optimal solution with data partition and without data partition. According to the obtained estimation, minimizing the similarity of the instances in the different divided subsets can largely reduce the effect of data partition. The minibatch k -means clustering algorithm is chosen to perform data partition for its effectiveness and efficiency. Finally, the nearest neighbors of the test instance are continuously searched from the set generated by successively merging the candidate subsets until they do not change anymore, where the candidate subsets are selected based on the similarity between the test instance and cluster centers. Experiment results on public datasets show that the proposed algorithm can largely keep the same nearest neighbors and no significant difference in classification accuracy as the original kNN classification algorithm and better results than two state-of-the-art algorithms.

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Section: Related Workmentioning

confidence: 99%

A Large-Scale $k$ -Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation

Song

Kong

Zhang

2022

Wireless Communications and Mobile Computing

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“…Instance selection deals with searching for a small subset S of the original training set T , such that a classifier trained on S shows similar, or even better classification performance than a classifier trained on the full data set T [9,2,11,7]. We will present confidence-based instance selection criteria for probabilistic support vector machines based on cross-validation.…”

Section: Introductionmentioning

confidence: 99%

On Instance Selection in Audio Based Emotion Recognition

Meudt¹,

Schwenker²

2012

Artificial Neural Networks in Pattern Recognition

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Affective computing aim to provide simpler and more natural interfaces for human-computer interaction applications, e.g. recognizing automatically the emotional status of the user based on facial expressions or speech is important to model user as complete as possible in order to develop human-computer interfaces that are able to respond to the user's action or behavior in an appropriate manner. In this paper we focus on audio-based emotion recognition. Data sets employed for the statistical evaluation have been collected through Wizard-of-Oz experiments. The emotional labels have been are defined through the experimental set up therefore given on a relatively coarse temporal scale (a few minutes) which This global labeling concept might lead to miss-labeled data at smaller time scales, for instance for window sizes uses in audio analysis (less than a second). Manual labeling at these time scales is very difficult not to say impossible, and therefore our approach is to use the globally defined labels in combination with instance/sample selection methods. In such an instance selection approach the task is to select the most relevant and discriminative data of the training set by using a pre-trained classifier. Mel-Frequency Cepstral Coefficients (MFCC) features are used to extract relevant features, and probabilistic support vector machines (SVM) are applied as base classifiers in our numerical evaluation. Confidence values to the samples of the training set are assigned through the outputs of the probabilistic SVM.

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From Supervised Instance and Feature Selection Algorithms to Dual Selection: A Review

Ros

Guillaume²

2019

Sampling Techniques for Supervised or Unsupervised Tasks

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Large scale instance selection by means of federal instance selection

Cited by 17 publications

References 28 publications

A Large-Scale $k$ -Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation

A Large-Scale $k$ -Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation

On Instance Selection in Audio Based Emotion Recognition

From Supervised Instance and Feature Selection Algorithms to Dual Selection: A Review

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Large scale instance selection by means of federal instance selection

Cited by 17 publications

References 28 publications

A Large-Scale k -Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation

A Large-Scale k -Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation

On Instance Selection in Audio Based Emotion Recognition

From Supervised Instance and Feature Selection Algorithms to Dual Selection: A Review

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Product

Resources

About

A Large-Scale $k$ -Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation

A Large-Scale $k$ -Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation