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LVQ-SMOTE - Learning Vector Quantization based Synthetic Minority Over-sampling Technique for biomedical data.

Nakamura M, Kajiwara Y, Otsuka A, Kimura H - BioData Min (2013)

Bottom Line: In order to improve the effectiveness of SMOTE, this paper presents a novel over-sampling method using codebooks obtained by the learning vector quantization.In general, even when an existing SMOTE applied to a biomedical dataset, its empty feature space is still so huge that most classification algorithms would not perform well on estimating borderlines between classes.Experiments on datasets for β-turn types prediction show some important patterns that have not been seen in previous analyses.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Natural Science and Engineering, Kanazawa University, Ishikawa 9200941, Japan. m-nakamura@blitz.ec.t.kanazawa-u.ac.jp.

ABSTRACT

Background: Over-sampling methods based on Synthetic Minority Over-sampling Technique (SMOTE) have been proposed for classification problems of imbalanced biomedical data. However, the existing over-sampling methods achieve slightly better or sometimes worse result than the simplest SMOTE. In order to improve the effectiveness of SMOTE, this paper presents a novel over-sampling method using codebooks obtained by the learning vector quantization. In general, even when an existing SMOTE applied to a biomedical dataset, its empty feature space is still so huge that most classification algorithms would not perform well on estimating borderlines between classes. To tackle this problem, our over-sampling method generates synthetic samples which occupy more feature space than the other SMOTE algorithms. Briefly saying, our over-sampling method enables to generate useful synthetic samples by referring to actual samples taken from real-world datasets.

Results: Experiments on eight real-world imbalanced datasets demonstrate that our proposed over-sampling method performs better than the simplest SMOTE on four of five standard classification algorithms. Moreover, it is seen that the performance of our method increases if the latest SMOTE called MWMOTE is used in our algorithm. Experiments on datasets for β-turn types prediction show some important patterns that have not been seen in previous analyses.

Conclusions: The proposed over-sampling method generates useful synthetic samples for the classification of imbalanced biomedical data. Besides, the proposed over-sampling method is basically compatible with basic classification algorithms and the existing over-sampling methods.

No MeSH data available.


Flow of the proposed over-sampling method. The numbered methods are executed in ascending sequence.
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Figure 2: Flow of the proposed over-sampling method. The numbered methods are executed in ascending sequence.

Mentions: Figure 2 shows a flow of the proposed method. As the figure shows, the proposed over-sampling method refers to a storage for codebooks extracted from reference datasets, and generates synthetic samples for a target dataset. First, we define the number of codebooks for each feature in the target dataset T as n and a set of two features in T as Ti (i=1,2,…,nc) where nc is the total number of the combinations of two features. Thus, each of Ti has n codebooks and two features. Next, regarding the numerical value of each codebook in T1 as T1(xj,yj) (j=1,2,…n), the sum of Euclidean distance between T1(xj,yj) and R1(xj,yj) of a reference dataset R is calculated. Figure 3 shows an example of Euclidean distances between T1(xj,yj) and R1(xj,yj). In this case, the sum of Euclidean distance between T1 and R1 is d1+d2. This procedure applies from R1 to all the set of two pairs in the storage. Then, T1 is linked to the set of two features which output the minimal sum of Euclidean distance.


LVQ-SMOTE - Learning Vector Quantization based Synthetic Minority Over-sampling Technique for biomedical data.

Nakamura M, Kajiwara Y, Otsuka A, Kimura H - BioData Min (2013)

Flow of the proposed over-sampling method. The numbered methods are executed in ascending sequence.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4016036&req=5

Figure 2: Flow of the proposed over-sampling method. The numbered methods are executed in ascending sequence.
Mentions: Figure 2 shows a flow of the proposed method. As the figure shows, the proposed over-sampling method refers to a storage for codebooks extracted from reference datasets, and generates synthetic samples for a target dataset. First, we define the number of codebooks for each feature in the target dataset T as n and a set of two features in T as Ti (i=1,2,…,nc) where nc is the total number of the combinations of two features. Thus, each of Ti has n codebooks and two features. Next, regarding the numerical value of each codebook in T1 as T1(xj,yj) (j=1,2,…n), the sum of Euclidean distance between T1(xj,yj) and R1(xj,yj) of a reference dataset R is calculated. Figure 3 shows an example of Euclidean distances between T1(xj,yj) and R1(xj,yj). In this case, the sum of Euclidean distance between T1 and R1 is d1+d2. This procedure applies from R1 to all the set of two pairs in the storage. Then, T1 is linked to the set of two features which output the minimal sum of Euclidean distance.

Bottom Line: In order to improve the effectiveness of SMOTE, this paper presents a novel over-sampling method using codebooks obtained by the learning vector quantization.In general, even when an existing SMOTE applied to a biomedical dataset, its empty feature space is still so huge that most classification algorithms would not perform well on estimating borderlines between classes.Experiments on datasets for β-turn types prediction show some important patterns that have not been seen in previous analyses.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Natural Science and Engineering, Kanazawa University, Ishikawa 9200941, Japan. m-nakamura@blitz.ec.t.kanazawa-u.ac.jp.

ABSTRACT

Background: Over-sampling methods based on Synthetic Minority Over-sampling Technique (SMOTE) have been proposed for classification problems of imbalanced biomedical data. However, the existing over-sampling methods achieve slightly better or sometimes worse result than the simplest SMOTE. In order to improve the effectiveness of SMOTE, this paper presents a novel over-sampling method using codebooks obtained by the learning vector quantization. In general, even when an existing SMOTE applied to a biomedical dataset, its empty feature space is still so huge that most classification algorithms would not perform well on estimating borderlines between classes. To tackle this problem, our over-sampling method generates synthetic samples which occupy more feature space than the other SMOTE algorithms. Briefly saying, our over-sampling method enables to generate useful synthetic samples by referring to actual samples taken from real-world datasets.

Results: Experiments on eight real-world imbalanced datasets demonstrate that our proposed over-sampling method performs better than the simplest SMOTE on four of five standard classification algorithms. Moreover, it is seen that the performance of our method increases if the latest SMOTE called MWMOTE is used in our algorithm. Experiments on datasets for β-turn types prediction show some important patterns that have not been seen in previous analyses.

Conclusions: The proposed over-sampling method generates useful synthetic samples for the classification of imbalanced biomedical data. Besides, the proposed over-sampling method is basically compatible with basic classification algorithms and the existing over-sampling methods.

No MeSH data available.