error in knn classification train accuracy is 96.33% but test accuracy is only 7.33%, is this overfitting problem.

error in knn classification train accuracy is 96.33% but test accuracy is only 7.33%, is this overfitting problem. how to solve this following is the code..

clear all; clc;

%% BUILD DORSAL HAND VEIN TEMPLATE DATABASE %build_db(10,8); %THIS WILL TAKE ABOUT 30 MINUTES

% % % load mat files load('db1.mat'); load('db2.mat'); %% reshape into row vector

reduced_testdata = reshape(reduced_testdata,1,5,16); % reduced_traindata = reshape(reduced_traindata,1,5,32); %

%% adjust dimension % reduced_testdata = reduced_testdata'; % Adjust matrix dimension P_test = cell2mat(reduced_testdata); % Convert cell array to matrix

%reduced_traindata = reduced_traindata'; P_train = cell2mat(reduced_traindata);

%%rearranges the dimensions of P_test and P_train so that they %are in the order specified by the vector ORDER.

C = permute(P_test,[1 3 2]); P_test = reshape(C,[],size(P_test,2),1);

C = permute(P_train,[1 3 2]); P_train = reshape(C,[],size(P_train,2),1);

%% labeling class train_label=load('train_label.txt'); test_label=load('test_label.txt');

%% Normalisation

P_train=P_train/256; P_test=P_test/256;

%% classfication

predictlabel = knnclassify(P_test, P_train, train_label,3,'euclidean','random'); cp = classperf(test_label,predictlabel);

% Evaluation performance disp('_____________Multiclass demo_______________') disp('Runing Multiclass confusionmat')

[FPR, TPR,Thr, AUC, OPTROCPT] = perfcurve(predictlabel, test_label,1)

 figure,
 plot(TPR,FPR,'r-','LineWidth',4);
 xlabel('False positive rate')
 ylabel('True positive rate') 
 title('ROC Curve for Classification ')
 Tbl = table(FPR, TPR,Thr)

%% FAR = FPR = FP/(FP + TN) and FRR = FNR = FN/(FN + TP)

[FPR, FNR,Thr] = perfcurve(predictlabel, test_label,1,'xCrit','FPR','yCrit','FNR','TVals','all') Thr = Thr/10; figure, plot(Thr,FPR,'r--','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g','MarkerSize',10); xlabel('Threshold') ylabel('False positive rate / FAR') title('ROC Curve for Classification ')

figure, plot(Thr,FNR,'r--','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g','MarkerSize',10); xlabel('Threshold') ylabel('False Negative rate / FRR') title('ROC Curve for Classification ') Tbl = table(FPR, FNR)

cp.CorrectRate*100 dca = predictlabel==test_label; peracc = sum(dca)/length(dca)*100; fprintf('\n\n Overall accuracy:%f%%\n',peracc);

[Greg Heath]

How many times have you tried using different random data divisions?