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Proposed method to decide the appropriate feature set for fish classification tasks using Artificial Neural Network and Decision Tree


Mutasem khalil Alsmadi, Khairuddin Bin Omar, Shahrul Azman Noah


Vol. 9  No. 3  pp. 297-301


We presents in this paper a novel fish classification methodology based on a robust feature selection technique using Artificial Neural Network and Decision Tree. Unlike existing works for fish classification, which propose descriptors and do not analyze their individual impacts in the whole classification task, we propose a general set of features and their correspondent weights that should be used as a priori information by the classifier. In this sense, instead of studying techniques for improving the classifiers structure itself, we consider it as a ""black box"" and focus our research in the determination of which input information must bring a robust fish discrimination. The study area selected for our proposed method from department of fisheries Malaysia ministry of agricultural and Agro-based industry in putrajaya, Malaysia region currently, the database contains several hundreds of fishes. In the future, we shall enhance the capability of the decision tree and ratification neural network classifier to deal with more than 2,000 fish species, which is the total amount of fish species along the coast of Malaysia. Data acquired on 22th August, 2008, is used. The classification problem involved the identification of 350 types of image fishes; family ,Scientific Name , English name , local name, Habitat , poison fish and non-poison, based on set of extraction feature .The main contribution of this paper is enhancement recognize and classify fishes based on digital image. Both classification and recognition are based on feature extraction.


Artificial Neural Network ,Decision Tree, multilayer-perceptron (MLP) ,Image Recognition, poison fish and non poison