%% Crab Classification % This demo illustrates using a neural network as a classifier to identify % the sex of crabs from physical dimensions of the crab. % Copyright 2003-2008 The MathWorks, Inc. % $Revision: 1.1.8.10 $ $Date: 2008/06/20 08:04:08 $ %% The Problem: Classification of Crabs % In this demo we attempt to build a classifier that can identify the sex % of a crab from its physical measurements. Six physical characterstics of % a crab are considered: species, frontallip, rearwidth, length, width and % depth. The problem on hand is to identify the sex of a crab given the % observed values for each of these 6 physical characterstics. % %% Why Neural Networks? % Neural networks have proven themselves as proficient classifiers and are % particularly well suited for addressing non-linear problems. Given the % non-linear nature of real world phenomena, like crab classification, % neural networks is certainly a good candidate for solving the problem. % % The six physical characterstics will act as inputs to a neural network % and the sex of the crab will be target. Given an input, which constitutes % the six observed values for the physical characterstics of a crab, the % neural network is expected to identify if the crab is male or female. % % This is achieved by presenting previously recorded inputs to a neural % network and then tuning it to produce the desired target outputs. This % process is called neural network training. % %% Preparing the Data % Data for classification problems can very often have textual or % non-numeric information. In our case, sex of the crab is non-numeric % (Male/Female). Neural networks however cannot be trained with non-numeric % data. Hence we need to translate the textual data into a numeric form. % % There are several ways to translate textual or symbolic data into numeric % data. Some of the common symbol translation techniques used are unary % encoding, binary encoding and numbering classes. We are going to use % unary encoding in this demo to perform symbol translation. fid = fopen('private/crabdata.csv'); C = textscan(fid,'%f%f%f%f%f%f%s','delimiter',','); % Import data fclose(fid); %% % The first 6 columns of data represent the crab's physical characterstics. % The 7th column represents the sex of the crab. physchars = [C{1} C{2} C{3} C{4} C{5} C{6}]; % inputs to neural network female = strncmpi(C{7}, 'Female', 1); male = strncmpi(C{7}, 'Male', 1); sex = double([female male]); % targets for neural network %% % 'Female' is now represented by the vector [1 0] and 'Male' is represented % by the vector [0 1]. This process is called unary encoding. The neural % network will now be trained to produce a [1 0] output for 'Female' crabs % and [0 1] output for 'Male' crabs. % % We could also have used numbers to represent the two sexes (Male=1, % Female=2) or performed binary encoding (Male=[0 0], Female=[0 1]). % % The next step is to preprocess the data into a form that can be used with % a neural network. % % The neural network object in the toolbox expects the samples along % columns and its features along rows. Our dataset has its samples along % rows and its features along columns. Hence the matrices have to be % transposed. physchars = physchars'; sex = sex'; %% Building the Neural Network Classifier % The next step is to create a neural network that will learn to identify % the sex of the crabs. % % Since the neural network starts with random initial weights, the results % of this demo will differ slightly every time it is run. The random seed % is set to avoid this randomness. However this is not necessary for your % own applications. rand('seed', 491218382) %% % A 1-hidden layer feed forward network is created with 20 neurons in the % hidden layer. % net = newff(physchars,sex,20); % Create a new feed forward network %% % Now the network is ready to be trained. The samples are automatically % divided into training, validation and test sets. The training set is % used to teach the network. Training continues as long as the network % continues improving on the validation set. The test set provides a % completely independent measure of network accuracy. [net,tr] = train(net,physchars,sex); %% Testing the Classifier % The trained neural network can now be tested with the testing samples % This will give us a sense of how well the network will do when applied % to data from the real world. testInputs = physchars(:,tr.testInd); testTargets = sex(:,tr.testInd); out = sim(net,testInputs); % Get response from trained network %% % The network response can now be compared against the desired target % response to build the classification matrix which will provides a % comprehensive picture of a classifiers performance. [y_out,I_out] = max(out); [y_t,I_t] = max(testTargets); diff = [I_t - 2*I_out]; f_f = length(find(diff==-1)); % Female crabs classified as Female f_m = length(find(diff==0)); % Female crabs classified as Male m_m = length(find(diff==-2)); % Male crabs classified as Male m_f = length(find(diff==-3)); % Male crabs classified as Female N = size(testInputs,2); % Number of testing samples fprintf('Total testing samples: %d\n', N); cm = [f_f f_m; m_f m_m] % classification matrix %% % The classification matrix provides a comprehensive picture of the % classification performance of the classifier. The ideal classification % matrix is the one in which the sum of the diagonal is equal to the number % of samples. % % It can also be understood in terms of percentages. The following matrix % provides the same information as above but in terms of percentages. cm_p = (cm ./ N) .* 100 % classification matrix in percentages fprintf('Percentage Correct classification : %f%%\n', 100*(cm(1,1)+cm(2,2))/N); fprintf('Percentage Incorrect classification : %f%%\n', 100*(cm(1,2)+cm(2,1))/N); %% % This demo illustrated using a neural network to classify crabs. % % Explore other demos and the documentation for more insight into neural % networks and its applications. displayEndOfDemoMessage(mfilename)