Age Prediction and Performance Comparison by Adaptive Network based Fuzzy Inference System using Subtractive Clustering

Age Prediction and Performance Comparison by Adaptive Network based Fuzzy Inference System using Subtractive Clustering Manisha Pariyani* & Kavita Burse** *M.Tech Scholar, department of Computer Science & Engineering, Oriental College of Technology, Bhopal, Madhya Pradesh, INDIA. E-Mail: manishap0707@gmail.com **Director, Oriental College of Technology, Bhopal, Madhya Pradesh, INDIA. E-Mail: kavitaburse14@gmail.com Abstract To integrate the best features of fuzzy systems and neural networks, a data mining approach with ANFIS is applied on all features of Abalone and Monk s problem dataset. The main aim of this research is to reduce the with fewer numbers of rules in order to achieve high speed and less time consumed in both learning and application phases. For calculating effective, an adaptive fuzzy inference system with subtractive clustering is proposed. Effective partition of input space is done and loaded into the ANFIS editor. A fuzzy inference system is generated using subtractive clustering and of training and testing is calculated by hybrid approach which is combination of back propagation and least square method. A structure is generated which shows input and output data along with number of fuzzy rules. This result into lower with fewer numbers of rules shows that ANFIS is well suited for age prediction of abalone and performance comparison of learning algorithms. Keywords Abalone; ANFIS; Fuzzy Rule; Monk s Problem; Root Mean Square Error; Subtractive Clustering. Abbreviations Adaptive Network based Fuzzy Inference System (ANFIS); Root Mean Square Error (). I. INTRODUCTION T HE architecture and learning procedure underlying ANFIS is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. The proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs by using a hybrid approach. ANFIS is best tradeoff between neural and fuzzy systems providing smoothness and adaptability. The objective of this research is to reduce the with fewer numbers of rules in order to achieve high speed and less time consumed in both learning and application phases. Neural networks and fuzzy systems both are stand-alone systems. ANFIS is one of the Neuro-fuzzy models. With the increase in the complexity of the process being modeled, the difficulty in developing dependable fuzzy rules and membership functions increases. This has led to the development of another approach which is mostly known as ANFIS approach. A hybrid system named ANFIS has been proposed by Jang (1993). It has the benefits of both fuzzy logic [Junhong Nie & Derek Linkens, 1998] and neural networks [James A. Anderson, 2002]. Fuzzy inference in this system is realized with the aid of a training algorithm, which enables to tune the parameters of the fuzzy system. In this paper, firstly the training and testing data of abalone [archive.ics.uci.edu/ml/datasets.html] and monk s problem dataset [archive.ics.uci.edu/ml/datasets.html] are divided. Then they are loaded into the ANFIS editor. After loading training and testing data a fuzzy inference system is generated using subtractive clustering. is calculated by training the network using hybrid approach which is combination of back propagation and least square method. Then for training against testing is noted. Finally structure is obtained which shows number of inputs and outputs along with number of fuzzy rules. It is developed in the MATLAB V7.9.0.529 (R2009b) [MATLAB] environment. II. RELATED WORK Ravi Jain & Ajith Abraham (2003) examined the performance of four fuzzy rule generation methods that could ISSN: 2321 2381 2013 Published by The Standard International Journals (The SIJ) 136

generate fuzzy if-then rules directly from training patterns with no time consuming tuning procedures. Shibendu Shekhar Roy (2005) proposed ANFIS for predicting the surface roughness in turning operation for set of giving cutting parameters. Two different membership functions triangular and bell shaped were adopted during the training process of ANFIS in order to compare the prediction accuracy of surface roughness by two membership functions Sean N.Ghazavi & Thunshun W.Liao (2008) proposed a study of medical data mining that involves the use of eleven feature selection methods and three fuzzy modeling methods, the objective is to determine which combination of feature selection and fuzzy modeling method has the best performance for a given dataset. Three rules are needed to obtain the classification rate 97% when using a modified fuzzy c-means radial basis functions network proposed by Essam Al-Daoud (2010). Pejman Tahmasebi & Ardeshir Hezarkhani (2010) proposed the adaptive Neuro-fuzzy inference system as a newly applied technique to solve such a problem to evaluate the "copper grade estimation" in Sarcheshmeh porphyry copper system. Fuzzy logic was invented by Zadeh (2010) for handling uncertain and imprecise knowledge in real world applications. It has proved to be a powerful tool for decisionmaking, and to handle and manipulate imprecise and noisy data. Mehdi Khashei et al., (2011) proposed a hybrid approach that combines artificial intelligence with fuzzy in order to benefit from unique advantages of both fuzzy logic and the classification power of the artificial neural networks to construct an efficient and accurate hybrid classifier in less available data situations. Jesmin Nahar et al., (2012) presented a rule extraction experiment on heart disease data using different rule mining algorithms (Apriori, Predictive Apriori and Tertius). Further rule-mining-based analysis was undertaken by categorising data based on gender and significant risk factors for heart disease. Castanho et al., (2012) proposed fuzzy expert system for predicting pathological stage of prostate cancer. A fuzzy expert system was developed with the fuzzy rules and membership functions tuned by a genetic algorithm. As a result, the utilized approach reached better precision taking into account some correlated studies. III. FUZZY NEURO SYSTEMS This section describes fuzzy inference system along with ANFIS architecture. The steps of inference operations upon fuzzy if-then rules (fuzzy reasoning) performed by fuzzy inference systems are: To obtain the membership values (or compatibility measures) of each linguistic label, compare the input variables with the membership functions on the premise part (This step is known as fuzzification). To get firing strength (weight) of each rule, combine (through a specific T-norm operator, usually multiplication or min.) the membership values on the premise part. Depending on the firing strength, generate the qualified consequent (either fuzzy or crisp) of each rule. To produce a crisp output, aggregate the qualified consequents (This step is known as defuzzification). 3.2. ANFIS Architecture To facilitate learning and adaptation, ANFIS is a fuzzy Sugeno model put in the framework of adaptive systems. The Sugeno fuzzy model was proposed by Takagi & Sugeno in an effort to formalize a systematic approach to generating fuzzy rules from an input-output data set. There are broadly three types of Fuzzy reasoning Models. They are Mamdani fuzzy models, Sugeno fuzzy models (TSK model) and Tsukamoto fuzzy models. The Mamdani fuzzy models and Sugeno fuzzy models are most widely used. Sugeno model is widely used in ANFIS because its rules are tunable based on input parameters. A typical fuzzy rule in a Sugeno fuzzy model has the format IF x is A and y is B THEN z = f(x, y), Where A and B are fuzzy sets in the antecedent; z = f(x, y) is a crisp function in the consequent, f(x, y) is a polynomial in the input variables x and y, but it can be any other functions that can appropriately describe the output of the system within the fuzzy region specified by the antecedent of the rule. If f(x, y) is a first-order polynomial, then model is called as the first-order Sugeno fuzzy model. If f is a constant, then it is called the zero-order Sugeno fuzzy model, which can be viewed either as a special case of the Mamdani fuzzy inference system, where each rule s consequent is specified by a fuzzy singleton, or a special case of Tsukamoto s fuzzy model where each rule s consequent is specified by a membership function of a step function centered at the constant Moreover, a zero order Sugeno fuzzy model is functionally equivalent to a radial basis function network under certain minor constraints. 3.1. Fuzzy Inference System A fuzzy inference system is composed of five functional blocks- a rule base containing a number of fuzzy if-then rule, a database which defines the membership functions, a decision-making unit which performs the inference, a fuzzification interface which transforms the crisp inputs, a defuzzification interface which transform the fuzzy. Figure 1: ANFIS Architecture ISSN: 2321 2381 2013 Published by The Standard International Journals (The SIJ) 137

Layer 1: For each node i n this layer generates membership grades of a linguistic label. For instance, the node function of the i th node may be a generalized bell membership function: µai x = 1 1 + x ci 2 bi ai Layer 2: Firing strength of a rule is calculated by each node via multiplication and the nodes are fixed in this layer wi = µai x µbi y ; i = 1,2 Layer 3: The nodes are fixed nodes. They are labeled with N, indicating that they play a normalization role to the firing strengths from the previous layer. The outputs of this layer can be represented as: wi = wi + w2; i = 1,2 w1 Layer 4: The output of each node in this layer is simply the product of the normalized firing strength and a first-order polynomial (for a first-order Sugeno model). Here the nodes are adaptive nodes. Thus, the outputs of this layer are given as: Oi x = wifi = wi(pix + qiy + ri) Layer 5: There is only one single fixed node labeled with S. This node performs the summation of all incoming signals. O1 x = overall output = wifi = wifi/ wi IV. METHODS This section includes ANFIS learning method and subtractive clustering 4.1. ANFIS Learning Method To learn, or adjust weights on connecting arrows between neurons from input-output training samples, neural networks, the back propagation algorithm are used. The ANFIS learning algorithm consists of adjusting the premises and consequents parameters. In the ANFIS structure, the parameters of the premises and consequents play the role of weights. Specifically, the shape of membership functions in the If part of the rules is determined by a finite number of parameters. These parameters are known as premise parameters, whereas the parameters in the THEN part of the rules are referred to as consequent parameters. For ANFIS, a combination of back propagation and Least Square Estimation (LSE) is used. Back propagation is used to learn the premise parameters, and LSE is used to determine the parameters in the rules consequents. A step in the learning procedure has two passes-forward and backward pass. In the forward pass, node outputs go forward, the premise parameters remain fixed while the consequent parameters {pi, qi, ri} are estimated by least squares method. In the backward pass the error signals are propagated backwards, consequent parameters remain fixed while the back propagation is used to modify the premise parameters {ai, bi, ci}. This combination of least-squares and back propagation methods are used for training FIS membership function parameters to model a given set of input/output data. The performance of this system will be evaluated using, root mean square errors (difference between the FIS output and the training/testing data output), which is defined as =1/n[ yk ok 2 ] 4.2. Subtractive Clustering When there is no clear idea how many clusters there should be for a given set of data, subtractive clustering is used. Subtractive clustering operates by finding the optimal data point to be defined as a cluster center, based on the density of surrounding data points. In order to determine the next data cluster and its center, all data points within the radius distance of these points are then removed. This process is repeated until all of the data is within the radius distance of a cluster center. When number of inputs is larger this method is used for rule generation. It gives optimized rules by taking into radii specified. It is a fast, one-pass algorithm for estimating the number of clusters and cluster centers in a set of data. It generates FIS structure by scatter partitioning. Here rules are predetermined by fixing number of centers. Membership functions were assigned automatically by software. Therefore, number of tuning parameters is reduced in this case by reducing number of rules. 5.1. Abalone Dataset V. RESULTS Abalone dataset contains 4177 entries in which each entry records the features of an abalone together with its age as the desired output. It contains 8 features of an abalone's physical measurements, with no missing data and28 classes corresponding to the age from 1 to 29 years of abalones. The age of abalone is determined by cutting the shell through the cone, staining and counting the number of rings through a microscope --a boring time-consuming task. Other measurements, which are easier to obtain, are used to predict the age. ISSN: 2321 2381 2013 Published by The Standard International Journals (The SIJ) 138

Table 1: Results on Abalone Dataset Train Linear Non-Linear Train Against Epoch Data Data Rules Parameters Parameters Nodes Train 500 500 50 3 24 42 58 8.3* 8.3* 8.3* 10^-005 500 1000 50 3 24 42 58 8.3* 8.3* 1.7531 56 500 50 9 72 126 154 1.7* 1.7* 1.4342 532 1000 50 2 16 28 42 0.22518 0.22518 1.2672 155 1000 50 3 24 42 58 0.71564 0.71564 1.021 374 500 50 3 24 42 58 9.1* 9.1* 1.5025 169 500 50 3 24 42 58 9.7* 9.7* 0.00014656 5.2. Monk s Problem Dataset Monks problem dataset is a multivariate dataset. It contains 432 instances, 7 attributes with no missing values. The Monk s problem was the basis of a first international Train Data Data Epoch Rules Linear Parameters Table 2: Results on Monk s Problem Dataset Non-Linear Parameters comparison of learning algorithms. There are three Monk's problems. The domains for all MONK's problems are the same one of the Monk's problems has noise added. For each problem, the domain has been partitioned into a train and test set. Nodes Train Train Against 25 50 50 11 77 132 163 1.3701 25 200 50 11 77 132 163 1.0532 50 150 50 32 224 384 457 0.35799 0.35799 1.0691 125 200 50 42 294 504 597 0.39216 0.39216 2.2923 25 257 50 11 77 132 163 0.9509 125 257 50 42 294 504 597 0.39216 0.39216 1.8571 25 125 50 11 77 132 163 1.2351 Training and testing data are partitioned. Epochs are kept fixed. Non linear parameters are fixed during forward stroke and steepest descent during backward stroke. Linear parameters are least square during forward stroke and fixed during backward stroke. Lower along with less number of rules on training and testing of abalone and monk s problem dataset shows that ANFIS is well suited for age prediction of abalone and performance comparison of various learning algorithms. VI. CONCLUSION An adaptive fuzzy inference system with neural learning using subtractive clustering is proposed for calculation of. Effective partition of the input space along with subtractive clustering decreases the rule number and increases the speed in both learning and application phases. It also provides smoothness due to fuzzy control interpolation and adaptability due to neural network back propagation. Lower and less number of rules results into less time consumed and better performance evaluation along with high speed in learning and application phases of ANFIS. This shows that ANFIS is well suited for age prediction and performance comparison of learning algorithms. In future, reprocessing on dataset can be done through various techniques before applied to the ANFIS. REFERENCES [1] J-S.R. Jang (1993), ANFIS Adaptive Network based Fuzzy Inference System, IEEE Transactions on Systems, Man and Cybernatics, Vol. 23, No. 3, Pp. 665 685. [2] Junhong Nie & Derek Linkens (1998), Fuzzy-Neural Control, Printice-Hall of India. [3] James A. Anderson (2002) An Introduction to Neural Network, Prentice Hall of India. [4] MATLAB V7.9.0.529, R2009b. Neuron-Fuzzy Computing based on Fuzzy Logic. Toolbox, MATLAB Works. [5] Ravi Jain & Ajith Abraham (2003), A Comparative Study of Fuzzy Classification Methods on Breast Cancer Data, 7th International Work Conference on Artificial and Natural Neural Networks (IWANN 03), Spain. [6] Shibendu Shekhar Roy (2005), Design of Adaptive Neuro Fuzzy Inference System for Predicting Surface Roughness in Turning Operation, Journal of Scientific and Industrial Research, Vol. 64, Pp. 653 659. ISSN: 2321 2381 2013 Published by The Standard International Journals (The SIJ) 139

[7] Sean N.Ghazavi & Thunshun W.Liao (2008), Medical Data Mining by Fuzzy Modeling with Selected Features, Elsevier, Vol. 43, Pp.195 206. [8] Essam Al-Daoud (2010), Cancer Diagnosis using Modified Fuzzy Network, Universal Journal of Computer Science and Engineering Technology, Vol. 2, Pp.73 78. [9] Pejman Tahmasebi & Ardeshir Hezarkhani (2010), Application of Adaptive Neuro-Fuzzy Inference System for Grade Estimation, Australian Journal of Basic and Applied Sciences, Vol. 4, Pp.408 420. [10] L.A. Zadeh (2010), Fuzzy Logic, IEEE Computer, Vol. 21, Pp. 83 93. [11] Mehdi Khashei, Ali Zeinal Hamadani & Mehdi Bijari (2011), A Fuzzy Intelligent Approach to the Classification Problem in Gene Expression Data Analysis, Elsevier, Vol. 27, Pp. 465 474. [12] Jesmin Nahar, Tasadduq Imama, Kevin S. Tickle & Yi-Ping Phoebe Chen (2012), Association Rule Mining to Detect Factors which Contribute to Heart Disease in Males and Females, Elsevier, Vol. 40, Pp. 1086 1093. [13] M.J.P. Castanho, F. Hernandes, A.M. De Re, S. Rautenberg & A. Billis (2012), Fuzzy Expert System for Predicting Pathological Stage of Prostate Cancer, Elsevier, Vol. 40, Pp.466 470. [14] Abalone Dataset- archive.ics.uci.edu/ml/datasets.html [15] Monk s Problem Dataset-- archive.ics.uci.edu/ml/datasets.html Manisha Pariyani was born in Bhopal in 1989.She received the BE degree (with distinction) in computer science and engineering from Sagar Institute of Technology Research and Science, RGPV, Bhopal in 2011.She is currently pursuing M.Tech in computer science and engineering from Oriental College of Technology, RGPV, Bhopal. She has attended the national conference held in various institutes and presented papers in different research areas. Her research interests include neural networks, data mining, network intrusion detection and artificial intelligence. Dr. Kavita Burse was born in Bhopal in 1970.She received the ph.d. degree, M.Tech degree in electronics and communication from MANIT,Bhopal and B.E. degree from SGSITS,Indore.She has 18 years of teaching and industrial experience. Presently she is Director in Oriental College of Technology, Bhopal. She has 37 national and an international publication to her credit.she is a reviewer for IEEE, Elsevier and other prestigious journals. She is a member of CSI, IETE and ISTE. Her Areas of Interest are E- Learning, Digital Signal Processing, Digital Communication and Neural Networks. ISSN: 2321 2381 2013 Published by The Standard International Journals (The SIJ) 140