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Improving the ID3 Algorithm By Filtering Out Attributes With Values Of 0 or 1
(2022-01-01)
Chuenprasertsuk, Pinyarat
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Jearanaitanakij, Kietikul
The iterative Dichotomiser 3 (ID3) algorithm is a classification algorithm that generates a decision tree, and it is one of the most simple and well-known tools. However, there is still room for improvement. This paper aims to reduce the running time of generating a decision tree by ignoring attributes that have an information gain value of 0 or 1 and the experiment result shows that the improved ID3 algorithm can significantly reduce the running time by more than 10 percent when compared with the traditional ID3 algorithm.
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Discovery of significant porcine SNPs for swine breed identification by a hybrid of information gain, genetic algorithm, and frequency feature selection technique
(2020-05-26)
Pasupa, Kitsuchart
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Rathasamuth, Wanthanee
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Tongsima, Sissades
Background: The number of porcine Single Nucleotide Polymorphisms (SNPs) used in genetic association studies is very large, suitable for statistical testing. However, in breed classification problem, one needs to have a much smaller porcine-classifying SNPs (PCSNPs) set that could accurately classify pigs into different breeds. This study attempted to find such PCSNPs by using several combinations of feature selection and classification methods. We experimented with different combinations of feature selection methods including information gain, conventional as well as modified genetic algorithms, and our developed frequency feature selection method in combination with a common classification method, Support Vector Machine, to evaluate the method's performance. Experiments were conducted on a comprehensive data set containing SNPs from native pigs from America, Europe, Africa, and Asia including Chinese breeds, Vietnamese breeds, and hybrid breeds from Thailand. Results: The best combination of feature selection methods - information gain, modified genetic algorithm, and frequency feature selection hybrid - was able to reduce the number of possible PCSNPs to only 1.62% (164 PCSNPs) of the total number of SNPs (10,210 SNPs) while maintaining a high classification accuracy (95.12%). Moreover, the near-identical performance of this PCSNPs set to those of bigger data sets as well as even the entire data set. Moreover, most PCSNPs were well-matched to a set of 94 genes in the PANTHER pathway, conforming to a suggestion by the Porcine Genomic Sequencing Initiative. Conclusions: The best hybrid method truly provided a sufficiently small number of porcine SNPs that accurately classified swine breeds.
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A Modified Binary Flower Pollination Algorithm: A Fast and Effective Combination of Feature Selection Techniques for SNP Classification
(2019-10-01)
Rathasamuth, Wanthanee
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Pasupa, Kitsuchart
Single nucleotide polymorphism (SNP) is a genetic trait responsible for the differences in the characteristics of individuals of a living species. Machine learning has been brought in to classify swine breed according to their SNPs. However, since the number of samples (number of pigs sampled) is usually much smaller than the number of features (SNPs) to classify, there may occur an overfitting problem. Therefore, some feature selection techniques were applied to the entire SNPs to reduce them to a much smaller number of most significant SNPs to be used in the classification. In this study, we used information gain in combination with binary flower pollination algorithm for feature selection as well as a cut-off-point-finding threshold for specifying a 0 or 1 value for a position in the solution vector and a GA bit-flip mutation operator. We called it Modified-BFPA. The classifier was SVM. Evaluated against a few other feature selection techniques, our combination of techniques was, at the very least, competitive to those. It selected only 1.76 % of most significant SNPs from the entire set of 10,210 SNPs. The SNPs that it selected provided 95.12 % classification accuracy. Moreover, it was fast: an average of 1.60 iterations in combination with SVM to find a set of best SNPs that provided the highest classification accuracy.
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Selection of a minimal number of significant porcine snps by an information gain and genetic algorithm hybrid model
(2019-01-01)
Rathasamuth, Wanthanee
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Pasupa, Kitsuchart
;
Tongsima, Sissades
A panel of a large number of common Single Nucleotide Polymorphisms (SNPs) distributed across an entire porcine genome has been widely used to represent genetic variability of pigs. With the advent of SNP-array technology, a genome-wide genetic profile of a specimen can be easily observed. Among the large number of such variations, there exists a much smaller subset of the SNP panel that could equally be used to correctly identify the corresponding breed. This work presents a SNP selection heuristic that can still be used effectively in the breed classification. The features were selected by combining a filter method and a wrapper method-information gain method and genetic algorithm-plus a feature frequency selection step, while classification used a support vector machine. We were able to reduce the number of significant SNPs to 0.86 % of the total number of SNPs in a swine dataset with 94.80 % classification accuracy.
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Hidden unit reduction of artificial neural network on English capital letter recognition
(2006-12-01)
Jearanaitanakij, Kietikul
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Pinngern, Ouen
We present an analysis on the minimum number of hidden units that is required to recognize English capital letters of the artificial neural network. The letter font that we use as a case study is the System font. In order to have the minimum number of hidden units, the number of input features has to be minimized. Firstly, we apply our heuristic for pruning unnecessary features from the data set. The small number of the remaining features leads the artificial neural network to have the small number of input units as well. The reason is a particular feature has a one-to-one mapping relationship onto the input unit. Next, the hidden units are pruned away from the network by using the hidden unit pruning heuristic. Both pruning heuristic is based on the notion of the information gain. They can efficiently prune away the unnecessary features and hidden units from the network. The experimental results show the minimum number of hidden units required to train the artificial neural network to recognize English capital letters in System font In addition, the accuracy rate of the classification produced by the artificial neural network is practically high. As a result, the final artificial neural network that we produce is fantastically compact and reliable. © 2006 IEEE.
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An information gain technique for acceleration of convergence of artificial neural networks
(2005-12-01)
Jearanaitanakij, Kietikul
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Pinngern, Ouen
This paper presents an application of information gain to accelerate the convergence time of Artificial Neural Networks (ANNs). We improve Hagiwara's convergence acceleration algorithm by applying information gain to it. The first step of our proposed technique is to calculate information gains of all features (or attributes) in training data and pass those gains through all hidden units in the next layer. During the training process, the algorithm monitors sum-squared error at the output layer. When the variation of sum-squared error becomes small, the worst hidden unit is detected. Next, all the weights connected to the worst hidden unit are reset to random values within the appropriate ranges. These ranges are determined by the propagated information gain of the worst hidden unit. Then, the network is retrained. When the number of weight resetting trials reaches a certain number, a new hidden unit is added to the network and the whole training process is repeated. Our experimental results on standard benchmarks show remarkable outputs in terms of convergence time. © 2005 IEEE.
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Determining the orders of feature and hidden unit prunings of artificial neural networks
(2005-12-01)
Jearanaitanakij, Kietikul
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Pinngern, Ouen
There is a great deal of research undertaken for pruning away features and hidden units in order to reduce the size of Artificial Neural Networks (ANNs). However, none of these methods mentions about the relationship between the pruned unit and the number of epochs needed for retraining when the unit is pruned away from the network. In this paper, we present two heuristics for determining the pruning orders, which lead to the near smallest number of retraining epochs. The heuristics are based on the employment of the modified information gain calculated from all features in training data. Then, we test our proposed heuristics on an exclusive-or data set. The experimental results show the success of using information gain as a criterion for determining the pruning orders. © 2005 IEEE.

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