A Survey on Hybrid Soft Computing Techniques based on Classification Problem

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 08, 2015 | ISSN (online): 2321-0613

A Survey on Hybrid Soft Computing Techniques based on Classification Problem Ankita Mishra1 Abha Choubey2 M.E. Scholar 2Associate Professor 1,2 Department of Computer Science & Engineering 1,2 SSTC Bhilai (C.G.) 1

Abstract—In this survey paper we survey about various techniques related to soft computing techniques useful for improve efficiency and accuracy of lots of complexity occur in classification techniques in data mining. In this survey we analyzed that multiple soft computing techniques provide different feature such as Generic algorithm techniques is based on optimizer function to upgrade neural net association and also include three components like Reproduction/Extraction, mutation, cross over etc. Similarly Neural network is purely based on artificial intelligence that provides the features to solve data classification problem through computing intellectual and Fuzzy logic is totally depend upon fuzzy set range starting from 0 to 1.We also analysis of hybrid concept of all soft computing techniques.

Key words: Neural Network, Genetic, Artificial Intelligence, fuzzy

excessively cumbering some throughout today's issues.

They generally require an unequivocally expressed expository model and regularly a great deal of computational time[2]. Soft computing methods, which underscore picks up in comprehension framework conduct in return for superfluous exactness, have turned out to be vital functional instruments for some contemporary issues.

Generic Algorithm and Particle Swarm Optimization Techniques have developed as potential and vigorous improvement apparatuses as of late. The course substance will be taught by famous specialists in the field, having satisfactory showing and research experience. This course will be valuable to staff from every building order as a potential registering device in their examination exercises.

This course is gone for recently enlisted educators (Less than five years’ Experience), composed from the fundamental ideas to application and surveys[1].

I. INTRODUCTION These days there is tremendous measure of Data being gathered and put away in databases all over the place over the globe. The inclination is to continue expanding quite a long time. It is not hard to discover databases with Terabytes of Data in undertakings and exploration offices. That is over 1,099,511, 627,776 bytes of Data. There is significant data and Data "covered up" in such databases; and without programmed strategies for extricating this Data it is for all intents and purposes difficult to dig for them. During the time numerous calculations were made to remove what is called pieces of learning from extensive arrangements of Data. There are a few distinct philosophies to approach this issue: Classification, Associations rule, Clustering, and so on[19].

Data Classification is a different procedure that includes different methods and criteria for sorting Data inside of a database or vault. This is for the most part done through a database or business knowledge programming that gives the capacity to sweep, distinguish and isolate information. A few samples and application of Data Classification include:  Distinguishing and keeping as often as possible utilized information as a part of plate/memory store[12].  Data sorting in view of substance/record sort, size and time of information[10].  Classifying so as to sort for security reasons information into limited, open or private information sorts[11].

Classification is used in many different area like bio-informatics, biology, genetics and Healthcare and so on.

Soft Computing Techniques (Artificial Neural Networks, Genetic Algorithms, Fuzzy Logic Models) have been perceived as appealing distinct options for the standard, entrenched "hard computing" ideal models.

Conventional hard computing routines are regularly

II. DIFFERENT TECHNIQUES OF SOFT COMPUTING Based on our analysis three basic techniques are used for Soft Computing: A. Genetic Algorithms: A Genetic techniques is based on a optimizer of a Function. to upgrade neural net association weight by GA, first we expect that the net's network has as of now been characterized and that every association weight can take a predefined limited scope of settings[1].

Three different components of Genetic Algorithm[16]:  Reproduction / Selection  Cross Over  Mutation In Genetic Algorithm generally first calculate fitness number. It is nothing but a objective function that explaining the suitability of a String. If a string is highly fitted should have low classification error[16]. Fitness function denoted as F(E):: F(E)=Emax - E1 where Emax is the maximum possible value of error function,E is the error term of objective function in multilayer perception that defined as follows: E=1/T ∑i∑j(rij-vij) where rij is resultant value for output node and vij is the original value for input node.

Fig. 1: Components of Generic Algorithm[1][12].

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A Survey on Hybrid Soft Computing Techniques based on Classification Problem (IJSRD/Vol. 3/Issue 08/2015/124)

1) Selection/Extraction: In this process purely based on calculated fitness number.

Generally this algorithm selected some particular form that can be use to create particular form of "fitness-dependent" probabilistic from the current density to produce particular for the resultant generation[13]. Extract feature or select some form of information(data) from the different kind of data or information through filtering the information this process generally called Selection process[12][13].This concept used in Pattern recognition and handwriting matching technique that can be provide optimized result[1][13].

and RBF system is more suitable to mirror the consequent criticism of the client[15].

III. DIFFERENT HYBRID COMPONENT OF SOFT COMPUTING METHODOLOGIES Soft computing is conceptually based on three key areas: Fuzzy logic, Neural network and probabilistic reasoning.

Many different techniques in this survey apply soft computing for solving classification problem and this classification one more technique will be produced called Hybrid techniques.

The main objective components focus for soft computing are as follows: A. Neurofuzzy: NP Define Neurofuzzy techniques is the combination of Neuro Computing and Fuzzy Logic. neuro-fuzzy system is a fuzzy system that uses a learning algorithm derived from or inspired by neural network theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples[11][14].

Fig. 2: ClassiďŹ cation System.

2) Mutation: In this process purely based on calculated fitness number.

Generally this algorithm selected some particular form that can be use to create particular form of "fitness-dependent" probabilistic from the current density to produce particular for the resultant generation[13]. Extract feature or select some form of information(data) from the different kind of data or information through filtering the information this process generally called Selection process[12][13].This concept used in Pattern recognition and handwriting matching technique that can be provide optimized result[1].

3) Cross Over: It is same as mutation process, that performed on pair to original strings to generate pair of offspring through a variation chose at a particular position called cross over position 4,if pair of strings 11001 and 00111 then generate result of offspring is 11011 and 00101[1][12][13].

B. Fuzzy Logic: In Fuzzy techniques is divided into different subset. Each subset is divided according to fuzzy rules. Fuzzy logic is a way to deal with registering in light of "degrees of truth" instead of the standard thing "genuine or false" (1 or 0) Boolean logic on which the cutting edge PC is based. It is generally improved accuracy of lots of data classifier. The evolutionary fuzzy rules based classifier is a fuzzy if-then system, as same as that can used in fuzzy control system.

C. Neural System: In Neural network based on Artificial Intelligence concept i.e. (ANN).Neural network is applied many different like the Neural Network-based Image Retrieval (NNIR) which is the human-PC collaboration framework model of content based mage recovery utilizing the Radial Basis Function (RBF) system. The RBF system is picked in this framework, in light of the fact that BP (Back Propagation) requires an expansive arrangement of arranged preparing information

B. Fuzzygenetic: FG The equations Fuzzy Genetic techniques is based on two concept such that Fuzzy Logic and Genetic Algorithm[1].Fuzzy techniques is the technique for solving optimization problem. After that apply Genetic algorithm to fuzzy optimization, fuzzy extreme stream issue and tunings the fuzzy controller[5][14][16].

C. Fuzzychaos: FCh Fuzzy logic and Chaos theory. "Fuzzy Chaotic Systems" gives unique heuristic exploration accomplishments and astute thoughts on the cooperation or inherent connections between fuzzy rationale and bedlam hypothesis. It shows the crucial ideas of fuzzy rationale and fuzzy control, tumult hypothesis and disorder control, and additionally the meaning of turmoil on the metric space of fuzzy sets. This monograph talks about and shows fuzzy displaying and fuzzy control of confused frameworks, synchronization, against control of chaos[15].

D. Neurogenetic: NG In this type of technique contain combination of Neural Networks and Genetic Algorithm .In neural network specially based on artificial intelligence. According to algorithm classifier are used[5]. In this introduce techniques, a feed forward poly network is needed. It is define BPN (Back propagation Network) technique. There must be multi-layer, no less than one shrouded layer and yield layer.

The concealed and yield layer hubs modify the weights worth relying upon the slip in characterization. In BPN the sign stream will be in food forward heading, yet the lapse is back engendered and weights are up dated to decrease blunder. The weights' adjustment is as per the blunder's slope bend, which indicates in the course the neighbourhood least. In this manner making it much dependable in expectation and also characterizing assignments[19].

E. Neurochaos: NCh NeuroChaos technology is referred to as Chaos principle, an experimental guideline portraying the capriciousness of frameworks. Its reason is that frameworks at times live in

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A Survey on Hybrid Soft Computing Techniques based on Classification Problem (IJSRD/Vol. 3/Issue 08/2015/124)

turmoil, creating vitality however with no consistency or bearing. They are exceptionally touchy to beginning conditions as outlined by the Butterfly Effect, whereby a solitary butterfly fluttering its wings can bring about a tornado most of the way over the world[19]. Illustrations of these frameworks incorporate the world's climate framework, the conduct of water bubbling on a stove or the transitory examples of feathered creatures[13]. While their clamorous conduct may seem arbitrary at to start with, disorganized frameworks can be characterized by a scientific recipe, and they are not without request or limited limits. Such frameworks are characterized as frameworks with Deterministic Chaos[1].

NeuroChaos considers the brain a complex, high dimensional biodynamic system of billions of interacting elements, with Deterministic Chaos. A proprietary method to assess the system has been developed and will be used to measure if the brain has been effected by certain events, like TBI. F. Fuzzyneurogenetic: FNG Fuzzy Logic and Neural Algorithm[1][6][16].

Networks

G. Neurofuzzygenetic: NFG Neural Networks and Fuzzy Algorithm[1][19].

Logic

and

and

Genetic

Genetic

H. Fuzzyprobabilistic: FP Neural Fuzzy Logic and Probabilistic reasoning[1][16].

IV. RESULT Proposed Method

Strength  

Fuzzy Logic

Artificial Neural Network

Evolutionary Computing Genetic Algorithm

             

Probabilistic Reasoning



Translate ability Tolerance to imprecision Plausibility Graduality Modeling Reasoning Transparency Adapattion Learning.

Approximaion Generalization ability Curve Fitting.

Fault Tolerance Global Optimization.

Computation Efficiency.

Weakness

 

Learning.

Knowledge acquisition.



Blackbox Interpretability.



Computational Speed.

Coding.

 

Well Understanding Rigorous Framework.

 

Lack of complete knowledge.

Computational complexity.

Limitation of

the axioms of probability theory



Discovering chaos in observed data.  Nonlinear dynamics  Chaos Simulation. identification.

Chaotic Algorithm  Prediction  Computation-al Strategy Complexity.

Formulation.  Determining the Predictability.

Table 1: Different Characteristics of the components of Soft Computing V. CONCLUSION The According to analysis of Table1 various techniques have many merits as well as demerit. The Neural network and Fuzzy logic techniques generate much faster and accurate result as compare to other techniques. Similarly Genetic algorithm is generate more optimizer result. All approach is fully based on classification problem.

The general approach can be applied for improve the quality of data complexity problem that can be caused during create classes.

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